Method and system for reputation management in peer-to-peer networks

Methods to minimize the impact of malicious peers on the performance of a peer-to-peer system. The system computes a global trust value for a peer by calculating the left principal eigenvector of a matrix of normalized local trust values, thus taking into consideration the entire system's history with each single peer. Algorithms for performing such computations in a scalable and distributed manner are provided. Rewarding highly reputable peers with better quality of service encourages non-malicious peers to share more information and to self-police their own information.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

System and method for personalized snippet generation

Snippets of text provided are generated based in part on a user's profile. An item, such as a document, is examined to identify terms related to the user's profile. A term profile for an identified term is compared to a user's profile. The more closely related the identified term is to the user's profile, the higher a similarity score will be. Alternatively, terms found in a document may have a user profile score which may be obtained by looking the term up in the user's profile. Terms having high profile similarity scores or high user profile scores are used in identifying snippets which may be relevant to a user. The high scoring terms may be added to search terms and provided to a snippet generator.

Method for detecting link spam in hyperlinked databases

Methods for facilitating the identification of link spamming in a linked database include calculating a spam likelihood value for nodes in a directed graph of linked nodes are disclosed. The spam likelihood value is computed from an importance of the node and a derivative value of the importance function with respect to a coupling factor. The likelihood that the node's importance is inflated by link spam is estimated by calculating the ratio of the magnitude of the derivative value for the node to the rank for the node. Alternatively, the spam likelihood may be computed directly from a component of the principal eigenvector of A evaluated at two values of the parameter c. The normalized derivative value can also be used to provide an order of importance in a list of nodes.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Variable Personalization of Search Results in a Search Engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Systems and methods for generating predicted queries and corresponding search results

A search system monitors the input of a search query by a user. Before the user finishes entering the search query, the search system identifies and sends a portion of the query as a partial query to the search engine. Based on the partial query, the search engine creates a set of predicted queries. This process may take into account prior queries submitted by a community of users, and may take into account a user profile. The predicted queries are to be sent back to the user for possible selection. The search system may also cache search results corresponding to one or more of the predicted queries in anticipation of the user selecting one of the predicted queries. The search engine may also return at least a portion of the search results corresponding to one or more of the predicted queries.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Social search engine

Methods, systems, and apparatus, including computer programs, for a social search engine. In one aspect, a method includes receiving an answer to a question from an answerer; identifying an entity in the answer, in which the entity refers to a product, a service, a company, or a merchant; obtaining permission from the answerer to provide an interactive link for the entity in the answer to a resource from which the entity can be obtained; receiving a user submitted query and determining that the query is related to the entity; and responsive to determining, providing the answer, including the link to the resource, as part of search results that are responsive to the query.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Variable personalization of search results in a search engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Customization of search results for search queries received from third party sites

A third party website provides a search interface to a general search engine. A site profile of the third party website describes various topics, keywords, or domains that are potentially relevant or of interest to users who access the third party website. The topics are associated with a topical directory, with domains associated with each topic; the domains in a given topic are given various weightings. When a search is submitted to the general search engine from the third party website via the search interface, the general search engine uses the site profile to customize the search results. The search results are customized by weighting the ranking of documents from websites associated with the topics in the site profile. The site profile can be manually or automatically constructed.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Method for detecting link spam in hyperlinked databases

Methods for facilitating the identification of link spamming in a linked database include calculating a spam likelihood value for nodes in a directed graph of linked nodes are disclosed. The spam likelihood value is computed from an importance of the node and a derivative value of the importance function with respect to a coupling factor. The likelihood that the node's importance is inflated by link spam is estimated by calculating the ratio of the magnitude of the derivative value for the node to the rank for the node. Alternatively, the spam likelihood may be computed directly from a component of the principal eigenvector of A evaluated at two values of the parameter c. The normalized derivative value can also be used to provide an order of importance in a list of nodes.

Targeted advertisements based on user profiles and page profile

Targeted advertisements are provided to an advertisement consumer based on a user profile, a page profile, or a combination thereof. In embodiment where a user of a search engine is an advertisement consumer, the user utilizes a search engine to obtain search results relevant to a search query. A user profile of the user's interests is used to select advertisements for inclusion with search results. The user profile is evaluated by an advertisement server which determines which advertiser(s) offers a highest price for the user profile. Advertisements from these advertisers are then selected. In another embodiment, where the user is accessing a page on a third party website, the page may include a request for advertisements. A page profile is evaluated by an advertisement server that determines which advertiser(s) offers a highest price for the page profile. Advertisements from these advertisers are then selected, and provided to the user, where they are included in the retrieved page.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Reputation of an author of online content

Methods, computer program products and systems are described for online-content management. Multiple online content items authored by multiple authors are received at one or more first computers for online publication. For each online content item, a reputation score is determined for the author of the online content item. The reputation score is based at least in part on one or more reviews of the online content item provided by one or more reviewers other than the author. In response to a query for online content received from a second computer, a set of search results is generated that includes an online content item from the multiple online content items. A ranking of the online content item in the set is determined based at least in part on the reputation score of the author.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Customization of search results for search queries received from third party sites

A third party website provides a search interface to a general search engine. A site profile of the third party website describes various topics, keywords, or domains that are potentially relevant or of interest to users who access the third party website. The topics are associated with a topical directory, with domains associated with each topic; the domains in a given topic are given various weightings. When a search is submitted to the general search engine from the third party website via the search interface, the general search engine uses the site profile to customize the search results. The search results are customized by weighting the ranking of documents from websites associated with the topics in the site profile. The site profile can be manually or automatically constructed.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Adaptive computation of ranking

A system and method is disclosed in which a ranking function for a set of document rank values is iteratively solved with respect to a set of linked documents until a first stability condition is satisfied. After such condition is satisfied, some of the ranks will have converged. The ranking function is modified to take into account these converged ranks so as to reduce the ranking function's computation cost. The modified ranking function is then solved until a second stability condition is satisfied. After such condition is satisfied more of the ranks will have converged. The ranking function is again modified and process continues until complete.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Generating website profiles based on queries from websites and user activities on the search results

In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Method and system for autocompletion using ranked results

A set of ordered predicted completion strings are presented to a user as the user enters text in a text entry box (e.g., a browser or a toolbar). The predicted completion strings can be in the form of URLs or query strings. The ordering may be based on any number of factors (e.g., a query's frequency of submission from a community of users). URLs can be ranked based on an importance value of the URL. Privacy is taken into account in a number of ways, such as using a previously submitted query only when more than a certain number of unique requestors have made the query. The sets of ordered predicted completion strings is obtained by matching a fingerprint value of the user's entry string to a fingerprint to table map which contains the set of ordered predicted completion strings.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Variable personalization of search results in a search engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Variable Personalization of Search Results in a Search Engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents. 1retrieving a set of documents relevant to the search query of a user, each document having an information retrieval score;determining a new information retrieval score of at least one document in the set of documents based on a user interest profile of the user; andranking the set of documents using the new information retrieval scores.. A computer-implemented method comprising: This application is a continuation of, and claims priority to, pending U.S. patent application Ser. No. 13/464,083, filed on May 4, 2012, entitled “Variable Personalization of Search Results in a Search Engine,” which is a continuation of, and claims priority to, U.S. patent application Ser. No. 12/720,479, filed on Mar. 9, 2010 and issued as U.S. Pat. No. 8/180,776, which claims the benefit of U.S. patent application Ser. No. 11/002,474, filed on Dec. 1, 2004 and issued as U.S. Pat. No. 7,716,223, which claims the benefit of U.S. Patent Application No. 60/557,783 filed on Mar. 29, 2004, which are both incorporated by reference herein. This application also incorporates by reference U.S. patent application Ser. No. 10/646,331 (filed on Aug. 22, 2003 and issued as U.S. Pat. No. 7,216,123) and Ser. No. 10/641,489 (filed on Aug. 14, 2003); all of the above applications are commonly owned along with this application.This ...

Customization of search results for search queries received from third party sites

A third party website provides a search interface to a general search engine. A site profile of the third party website describes various topics, keywords, or domains that are potentially relevant or of interest to users who access the third party website. The topics are associated with a topical directory, with domains associated with each topic; the domains in a given topic are given various weightings. When a search is submitted to the general search engine from the third party website via the search interface, the general search engine uses the site profile to customize the search results. The search results are customized by weighting the ranking of documents from websites associated with the topics in the site profile. The site profile can be manually or automatically constructed.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Personalizing anchor text scores in a search engine

A search engine identifies a list of documents from a set of documents in a database in response to a set of query terms. For each document in the list, the search engine determines an information retrieval score based on its content and the query terms, and also identifies a set of source documents that have links to the document and that also have anchor text satisfying a predefined requirement with respect to the query terms. The search engine calculates a personalized page importance score for each of the identified source documents according to a set of user-specific parameters and accumulates the personalized page importance scores to produce a personalized anchor text score for the document. The personalized anchor text score is then combined with the document's information retrieval score to generate a personalized ranking for the document. The documents are ordered according to their respective personalized rankings.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Generating Website Profiles Based on Queries from Websites and User Activities on the Search Results

In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders. 1. A computer-implemented method , comprising: receiving from a website search queries submitted by users of the website;', 'providing to the users search results responsive to the search queries;', 'processing activities of the users on the search results;', 'generating a website profile for the website using the processed activities on the search results by the users; and', 'modifying, based on the generated website profile, rankings of search results provided in response to new search queries from the website., 'at a server having memory and one or more processors2. The computer-implemented method of claim 1 , wherein the user activities include user selections of the search results and mouse hovering time on the search results.3. The computer-implemented method of claim 1 , wherein a subset of the search queries is used for generating the website profile by sub-sampling the search queries during a predefined time period.4. The computer-implemented method of claim 1 , wherein a subset of the search queries is used for generating the website profile by choosing search queries having a predefined range of occurrence frequency.5. The computer-implemented method of claim 1 , wherein temporally recent search queries ...

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Predicted query generation from partial search query input

A search system monitors the input of a search query by a user. Before the user finishes entering the search query, the search system identifies and sends a portion of the query as a partial query to the search engine. Based on the partial query, the search engine creates a set of predicted queries. This process may take into account prior queries submitted by a community of users, and may take into account a user profile. The predicted queries are to be sent back to the user for possible selection. The search system may also cache search results corresponding to one or more of the predicted queries in anticipation of the user selecting one of the predicted queries. The search engine may also return at least a portion of the search results corresponding to one or more of the predicted queries.

Method and System for Autocompletion Using Ranked Results

A set of ordered predicted completion strings are presented to a user as the user enters text in a text entry box (e.g., a browser or a toolbar). The predicted completion strings can be in the form of URLs or query strings. The ordering may be based on any number of factors (e.g., a query's frequency of submission from a community of users). URLs can be ranked based on an importance value of the URL. Privacy is taken into account in a number of ways, such as using a previously submitted query only when more than a certain number of unique requesters have made the query. The sets of ordered predicted completion strings is obtained by matching a fingerprint value of the user's entry string to a fingerprint to table map which contains the set of ordered predicted completion strings.

Targeted advertisements based on user profiles and page profile

Targeted advertisements are provided to an advertisement consumer based on a user profile, a page profile, or a combination thereof. In embodiment where a user of a search engine is an advertisement consumer, the user utilizes a search engine to obtain search results relevant to a search query. A user profile of the user's interests is used to select advertisements for inclusion with search results. The user profile is evaluated by an advertisement server which determines which advertiser(s) offers a highest price for the user profile. Advertisements from these advertisers are then selected. In another embodiment, where the user is accessing a page on a third party website, the page may include a request for advertisements. A page profile is evaluated by an advertisement server that determines which advertiser(s) offers a highest price for the page profile. Advertisements from these advertisers are then selected, and provided to the user, where they are included in the retrieved page.

Customization of search results for search queries received from third party sites

A third party website provides a search interface to a general search engine. A site profile of the third party website describes various topics, keywords, or domains that are potentially relevant or of interest to users who access the third party website. The topics are associated with a topical directory, with domains associated with each topic; the domains in a given topic are given various weightings. When a search is submitted to the general search engine from the third party website via the search interface, the general search engine uses the site profile to customize the search results. The search results are customized by weighting the ranking of documents from websites associated with the topics in the site profile. The site profile can be manually or automatically constructed.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Methods for ranking nodes in large directed graphs

Techniques for assigning ranks to nodes in a large linked database, such as world wide web or any other hypermedia database, partition the nodes so that the link matrix has a predominantly block-diagonal form. Within each block, a local rank is computed for nodes in the block, possibly by different computer in a distributed computing environment. A block rank is then estimated for each block as a whole, and may optionally include block-level weights to implement customized ranking. The local ranks and block ranks are then combined to form a global rank, which may be used to rank the nodes. Alternatively, a global rank vector for the database may be used as an initial vector in an iterative link-based ranking scheme to obtain more accurate global ranks for the nodes. The global rank vector may be divided to provide local rank vectors for use in subsequent applications of the method.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Меthоd аnd sуstеm fоr rеputаtiоn mаnаgеmеnt in pееr-tо-pееr nеtwоrks

Меthоds tо minimizе thе impасt оf mаliсiоus pееrs оn thе pеrfоrmаnсе оf а pееr-tо-pееr sуstеm. Тhе sуstеm соmputеs а glоbаl trust vаluе fоr а pееr bу саlсulаting thе lеft prinсipаl еigеnvесtоr оf а mаtriх оf nоrmаlizеd lосаl trust vаluеs, thus tаking intо соnsidеrаtiоn thе еntirе sуstеm's histоrу with еасh singlе pееr. Аlgоrithms fоr pеrfоrming suсh соmputаtiоns in а sсаlаblе аnd distributеd mаnnеr аrе prоvidеd. Rеwаrding highlу rеputаblе pееrs with bеttеr quаlitу оf sеrviсе еnсоurаgеs nоn-mаliсiоus pееrs tо shаrе mоrе infоrmаtiоn аnd tо sеlf-pоliсе thеir оwn infоrmаtiоn.

Monetization of Online Content

Methods, computer program products and systems are described for computer implemented online-content management. Multiple online content items authored by multiple authors for online publication are received at one or more first computers. For each online content item, a reputation score is determined for the author corresponding to the online content item. The reputation score is based at least in part on one or more reviews of the online content item provided by one or more reviewers other than the author. An online content item from the multiple online content items is published for display on one or more second computers, which includes displaying an advertisement in conjunction with displaying the online content item. A share of revenue for the author of the online content item for displaying the advertisement is determined based at least in part on the reputation score of the author.

Variable personalization of search results in a search engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Variable Personalization of Search Results in a Search Engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents. 1. A computer-implemented method , comprising:receiving an interest profile of a user, the interest profile comprising data representing topics of interest to the user;receiving a search query;receiving first documents responsive to the search query and associated with respective first ranking scores;receiving second documents responsive to the search query and associated with respective second ranking scores;combining the first documents and the second documents;generating a mapping between first documents and second documents;generating rankings of the combined documents, wherein each ranking is an ordering of the combined documents and wherein each ranking is based on the first ranking scores, the second ranking scores, and a degree of influence of the interest profile that is different from the degrees of influence associated with the other rankings; andproviding the mapping and the modified rankings to a user device.2. The computer-implemented method of wherein generating rankings of the combined documents comprises:generating a respective ranking of the combined documents for each degree of influence.3. The computer-implemented method of claim 1 , wherein the first ranking values are based on boost values claim 1 , wherein the boost values are calculated from a site graph claim 1 , wherein ...

System and Method for Personalized Snippet Generation

A method of producing search results is disclosed. The method comprises, at a computerized search engine system distinct from a client system: receiving a search request associated with a user from the client system, the search request having one or more search terms; obtaining a user profile corresponding to the user, where the user profile is generated based in part on the user's prior computing activities, comprising one or more of browsing, searching, and messaging; obtaining search results for the search request; generating a personalized snippet for at least one of the search results in accordance with the obtained user profile, the snippet comprising a text portion of the search result chosen based on at least one or more search terms and one or more terms of the obtained user profile; and transmitting the search results and personalized snippet to the client system for display.

System and method for personalized snippet generation

A method of producing search results is disclosed. The method comprises, at a computerized search engine system distinct from a client system: receiving a search request associated with a user from the client system, the search request having one or more search terms; obtaining a user profile corresponding to the user, where the user profile is generated based in part on the user's prior computing activities, comprising one or more of browsing, searching, and messaging; obtaining search results for the search request; generating a personalized snippet for at least one of the search results in accordance with the obtained user profile, the snippet comprising a text portion of the search result chosen based on at least one or more search terms and one or more terms of the obtained user profile; and transmitting the search results and personalized snippet to the client system for display.

Results based personalization of advertisements in a search engine

Anticipated query generation and processing in a search engine

A search system monitors the input of a search query by a user. Before the user finishes entering the search query, the search system identifies and sends a portion of the query as a partial query to the search engine. Based on the partial query, the search engine creates a set of predicted queries. This process may take into account prior queries submitted by a community of users, and may take into account a user profile. The predicted queries are be sent back to the user for possible selection. The search system may also cache search results corresponding to one or more of the predicted queries in anticipation of the user selecting one of the predicted queries. The search engine may also return at least a portion of the search results corresponding to one or more of the predicted queries.

Results based personalization of advertisements in a search engine

Personalized advertisements are provided to a user using a search engine to obtain documents relevant to a search query. The advertisements are personalized in response to a search profile that is derived from personalized search results. The search results are personalized based on a user profile of the user providing the query. The user profile describes interests of the user, and can be derived from a variety of sources, including prior search queries, prior search results, expressed interests, demographic, geographic, psychographic, and activity information.

User-created content aggregation and sharing

Methods, computer program products and systems are described for online content management. Features include receiving content from multiple contributors for public online display, authenticating authors of the content, receiving input from users other than authors of the content regarding the quality of the content and determining a reputation score, authentication score and/or credibility factor for authors of online content. A collaborative editor can also be provided for editing online content.

Customization of search results for search queries received from third party sites

A third party website provides a search interface to a general search engine. A site profile of the third party website describes various topics, keywords, or domains that are potentially relevant or of interest to users who access the third party website. The topics are associated with a topical directory, with domains associated with each topic; the domains in a given topic are given various weightings. When a search is submitted to the general search engine from the third party website via the search interface, the general search engine uses the site profile to customize the search results. The search results are customized by weighting the ranking of documents from websites associated with the topics in the site profile. The site profile can be manually or automatically constructed.

Results based personalization of advertisements in a search engine

Personalized advertisements are provided to a user using a search engine to obtain documents relevant to a search query. The advertisements are personalized in response to a search profile that is derived from personalized search results. The search results are personalized based on a user profile of the user providing the query. The user profile describes interests of the user, and can be derived from a variety of sources, including prior search queries, prior search results, expressed interests, demographic, geographic, psychographic, and activity information.

Method of generating a website profile based on monitoring user activities

In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Method of generating a website profile based on monitoring user activities

In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Improved methods for ranking nodes in large directed graphs

Techniques for assigning ranks (fig. 6, 630, 670, 680) to nodes in a large linked database (fig. 2) such as world wide web or any other hypermedia database, partition the nodes so that the link matrix (fig. 1) has a predominantly block-diagonal form (fig. 1&2). Within each block (fig. 5), a local rank (630, 670) is computed for nodes in the block (fig. 5), possibly by different computer in a distributed computing environment. A block rank is then estimated for each block (fig. 5) as a whole, and may optionally include block-level weights to implement customized ranking (fig. 6, 630, 670, 680). The local ranks and block ranks are then combined to form a global rank, which may be used to rank the nodes. Alternatively, a global rank vector for the database may be used as an initial vector in an iterative link-based ranking schema to obtain more accurate global ranks for the nodes. The global rank vector (fig. 6, 630, 670, 680) may be divided to provide local rank vectors (fig. 6, 630, 670, 680) for use in subsequent applications of the method.

Variable personalization of search results in a search engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their filly or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Improved methods for ranking nodes in large directed graphs

Techniques for assigning ranks (fig. 6, 630, 670, 680) to nodes in a large linked database (fig. 2) such as world wide web or any other hypermedia database, partition the nodes so that the link matrix (fig. 1) has a predominantly block-diagonal form (fig. 1&2). Within each block (fig. 5), a local rank (630, 670) is computed for nodes in the block (fig. 5), possibly by different computer in a distributed computing environment. A block rank is then estimated for each block (fig. 5) as a whole, and may optionally include block-level weights to implement customized ranking (fig. 6, 630, 670, 680). The local ranks and block ranks are then combined to form a global rank, which may be used to rank the nodes. Alternatively, a global rank vector for the database may be used as an initial vector in an iterative link-based ranking schema to obtain more accurate global ranks for the nodes. The global rank vector (fig. 6, 630, 670, 680) may be divided to provide local rank vectors (fig. 6, 630, 670, 680) for use in subsequent applications of the method.

Improved methods for ranking nodes in large directed graphs

Variable personalization of search results in a search engine

A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their filly or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Method of generating a website profile bases on monitoring user activities

In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Method for Detecting Link Spam in Hyperlinked Databases

A computer-implemented method identifies nodes that are beneficiaries of node importance inflating links in a directed graph of linked nodes. The directed graph of linked nodes corresponds to a linked database, and the nodes correspond to documents within the linked database. The method is performed by a computer system including one or more processors and memory storing one or more programs, the one or more processors executing the one or more programs to perform the method. The method includes computing, for each of at least a subset of the nodes in the directed graph, a respective quantity corresponding to a mathematical derivative of a node importance function, and performing a remedial action on a respective node in the directed graph in accordance with the respective computed quantity computed for the respective node.

System and method for personalized snippet generation

A method of producing search results is disclosed. The method comprises, at a computerized search engine system distinct from a client system: receiving a search request associated with a user from the client system, the search request having one or more search terms; obtaining a user profile corresponding to the user, where the user profile is generated based in part on the user's prior computing activities, comprising one or more of browsing, searching, and messaging; obtaining search results for the search request; generating a personalized snippet for at least one of the search results in accordance with the obtained user profile, the snippet comprising a text portion of the search result chosen based on at least one or more search terms and one or more terms of the obtained user profile; and transmitting the search results and personalized snippet to the client system for display.

System and method for personalized snippet generation

A method of producing search results is disclosed. The method comprises, at a computerized search engine system distinct from a client system: receiving a search request associated with a user from the client system, the search request having one or more search terms; obtaining a user profile corresponding to the user, where the user profile is generated based in part on the user's prior computing activities, comprising one or more of browsing, searching, and messaging; obtaining search results for the search request; generating a personalized snippet for at least one of the search results in accordance with the obtained user profile, the snippet comprising a text portion of the search result chosen based on at least one or more search terms and one or more terms of the obtained user profile; and transmitting the search results and personalized snippet to the client system for display.