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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ

Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Поддерживает ввод нескольких поисковых фраз (по одной на строку). При поиске обеспечивает поддержку морфологии русского и английского языка
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Применить Всего найдено 13. Отображено 12.
26-12-2013 дата публикации

MULTIMEDIA FEATURES FOR CLICK PREDICTION OF NEW ADVERTISEMENTS

Номер: US20130346182A1
Автор: Azimi Javad, Cheng Haibin, Manavoglu Eren, Navalpakkam Vidhya, Zhang Ruofei, Zhou Yang, Zwol Roelof van
Принадлежит: Yahoo! Inc.

Multimedia features extracted from display advertisements may be integrated into a click prediction model for improving click prediction accuracy. Multimedia features may help capture the attractiveness of ads with similar contents or aesthetics. Numerous multimedia features (in addition to user, advertiser and publisher features) may be utilized for the purposes of improving click prediction in ads with limited or no history. 1. A system for click prediction comprising:a publisher server for providing a page that includes at least one advertisement slot;an advertisement server for providing an advertisement; and an extractor that extracts multimedia features from the advertisement;', 'a comparator that compares at least one of the advertisement, the multimedia features, or the at least one advertisement slot with historical click history data; and', 'a modeler that utilizes a click prediction model that incorporates the multimedia features and the comparison with the historical click history data., 'a click predictor comprising2. The system of wherein the comparator compares the multimedia features of the advertisement with historical click history from advertisements with similar multimedia features.3. The system of wherein the modeler generates the click prediction model.4. The system of wherein the multimedia features comprise at least one of image features claim 1 , flash features claim 1 , mixture component features claim 1 , or conjunction features.5. The system of wherein the image features comprise global features that apply to an entire image or local features that apply to segments of the entire image.6. The system of wherein the image features comprise at least one of brightness claim 5 , saturation claim 5 , colorfulness claim 5 , naturalness claim 5 , contrast claim 5 , sharpness claim 5 , texture claim 5 , grayscale simplicity claim 5 , color simplicity claim 5 , color harmony claim 5 , or hue claim 5 , further wherein any of these image features ...

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Номер записи: 1
21-01-2016 дата публикации

Expansion of targeting criteria based on advertisement performance

Номер: US20160019581A1
Автор: Anh Phuong Bui, Boris Pierre Arnoux, Gang Wu, Haibin Cheng, Xiao Zhang
Принадлежит: Facebook Inc

An online system selects advertisements for presentation a user based on characteristics of the user. The online system monitors performance of advertisements based on a goal for the advertisement and a time interval for achieving the goal. During a time period within the time interval, the online system determines an actual performance of the advertisement and compares the actual performance to a portion of the goal associated with the time period. If the actual performance does not satisfy the portion of the goal associated with the time period, the online system expands targeting criteria of the advertisement to increase a number of users eligible to be presented with the advertisement.

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Номер записи: 2
21-01-2016 дата публикации

EXPANSION OF TARGETING CRITERIA USING AN ADVERTISEMENT PERFORMANCE METRIC TO MAINTAIN REVENUE

Номер: US20160019595A1
Автор: Arnoux Boris Pierre, Bui Anh Phuong, Cheng Haibin, Wu Gang, Zhang Xiao
Принадлежит:

An online system selects advertisements for a user based on characteristics of the user. The online system presents advertisements to the user having targeting criteria satisfied by the characteristics of the user. To increase the number of users eligible to be presented with an advertisement, the online system increases the users eligible to be presented with the advertisement to include users that do not meet targeting criteria included in the advertisement. The online system obtains a percentile of users based on a performance metric associated with the advertisement and determines a cutoff measure of affinity based on the percentile and measures of affinity between various users and the advertisement. A user is eligible to be presented with the advertisement if a measure of affinity between the user and the advertisement is greater than the cutoff measure of affinity for the advertisement.

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Номер записи: 3
13-04-2017 дата публикации

Advertisement Targeting for an Interest Topic

Номер: US20170103418A1
Автор: Dominique Marcel Georgette Van de Sompel, Haibin Cheng
Принадлежит: Facebook Inc

An advertising system identifies users associated with an interest topic and generates a list of such users in which all advertising accounts are proportionately represented in the list. Such users are identified by recording user-page access data to each page in a cluster of pages associated with the interest topic. A list of user-account associations is generated by grouping the user-page access data by the advertising account associated with each page. The list is then optimized so a proportion of user-account associations for each advertising account is less than or equal to a predetermined threshold. This ensures that no one advertising account is overrepresented in the list. Using the optimized list, the advertising system can target users associated with the list with advertisements related to the interest topic.

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Номер записи: 4
18-05-2017 дата публикации

LOOKALIKE EVALUATION

Номер: US20170140283A1
Автор: Cheng Haibin, Pei Yang, Xu Xian
Принадлежит:

Lookalike models can select users that are predicted to share characteristics with a specified set of seed users. The processing requirements for lookalike models can be decreased by identifying features that have low impact on model accuracy, and therefore can be excluded from creating models. Also, by identifying preferred seed sources and training parameters, accurate lookalike models can be created with less overhead and in less time. The features and training parameters can be identified by obtaining a sample seed set, extracting seeds with a defined set of features, and using the remaining training seeds to train a model. Performance of this model can be compared to a standard model to see if the model performs well. If so, features excluded from the features used to create the model, a seed source, or training parameters used to create the model can be selected. 1. A method for selecting preferred seed sources , comprising:obtaining multiple seed groups from multiple seed sources;selecting subsets of the seed groups as training seed groups;generating unions from the training seed groups by selecting training seeds that have common evaluation data;using two or more of the unions to train two or more corresponding lookalike models;comparing performances of the trained two or more lookalike models;selecting, based on the comparisons, as the preferred seed sources, the seed sources associated with the at least one of the trained two or more lookalike models that have a performance above a threshold level; andtraining one or more additional lookalike models using the selected preferred seed sources.2. The method of claim 1 , wherein the unions are obtained by applying a universal holdout to the training seed sets to select training seeds that have common evaluation data.3. The method of claim 2 , wherein applying the universal holdout comprises:merging the training seed sets;sorting the merged training seed sets on evaluation data types comprising cluster ID and/ ...

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Номер записи: 5
04-12-2014 дата публикации

METHOD AND APPARATUS FOR MEASURING SEDIMENTATION OF SOLID-LIQUID TWO-PHASE MIXTURE

Номер: US20140355645A1
Автор: Cheng Haibin, DAI YAWEN, Liu Chaosheng, MA Wentao, ZHANG Qingjie
Принадлежит: Wuhan University of Technology

A method and an apparatus for measuring sedimentation of a solid-liquid two-phase mixture are provided. A standard work curve and/or standard mathematical model, indicating a relationship between thermal conductivity (k) and concentration (φ) (and/or density (ρ)), are provided for measuring sedimentation of the solid-liquid two-phase mixture. To measure the sediment, thermal conductivities (k) are measured at settling times (t) to obtain a relationship (k−t). Concentrations (φ) and/or densities (ρ) are then determined, based on the measured relationship (k−t) and the standard work curve and/or the standard mathematical model. A sedimentation rate is determined according to a variation rate of the thermal conductivity. A sedimentation status, sedimentation degree, and/or complete sedimentation degree are determined according to variation rate and variation degree of the thermal conductivity (k), the concentration (φ) and/or the density (ρ) of the solid-liquid two-phase mixture to be measured. 1. A method for measuring sedimentation of a solid-liquid two-phase mixture , comprising:providing the solid-liquid two-phase mixture to be measured;providing one or more of a standard work curve and a standard mathematical model, wherein each of the standard work curve and the standard mathematical model provides a relationship between a thermal conductivity (k) and a concentration (φ) or a relationship between a thermal conductivity (k) and a density (ρ);measuring a thermal conductivity (k) of a sediment in the solid-liquid two-phase mixture to be measured at each of a plurality of settling times (t) to obtain a relationship curve (k−t);converting the relationship curve (k−t) into a relationship curve (φ−t) or a relationship curve (ρ−t) or both, based on the one or more of the standard work curve and the mathematical relationship; and determining a concentration (φ) or a density (ρ) of the solid-liquid two-phase mixture to be measured, based on the measured thermal ...

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Номер записи: 6
06-08-2020 дата публикации

CLUSTERING USERS OF A SOCIAL NETWORKING SYSTEM BASED ON USER INTERACTIONS WITH CONTENT ITEMS ASSOCIATED WITH A TOPIC

Номер: US20200250702A1
Автор: Cheng Haibin, Hong Sue Ann, YE Xingyao
Принадлежит: FACEBOOK, INC.

A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic. 120-. (canceled)21. A system , comprising:a processor; identify a cluster group of target users having a threshold measure of similarity to prior users who have previously interacted with prior digital content;', 'determine a likelihood that each of the target users in the cluster group will interact with new digital content by applying a machine learning model to the cluster group of target users based on the threshold measure of similarity; and', 'identify an opportunity to present new digital content to at least one of the target users based at least in part of the determined likelihood that the at least one of the target users will interact with the new digital content, the new digital content having similar targeting criteria as the prior digital content., 'a memory storing instructions, which when executed by the processor, cause the processor to22. The system of claim 21 , wherein the cluster group is identified based on:identifying at least one characteristic in the prior digital content with which prior users have ...

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Номер записи: 7
10-10-2019 дата публикации

RANKING PROPERTY LISTING SEARCH RESULTS

Номер: US20190311044A1
Автор: Cheng Haibin, Collins Brendan Marshall, Haldar Malay, Xu Tao
Принадлежит: AIRBNB, Inc.

An online reservation system is configured to receive requests from a guest for searching property listings and to return property listings that satisfy the search criteria of the requests. The online reservation system uses a machine learning system to rank the property listings returned by the search. The machine learning system uses objective functions to determine parameters for each property listing and assign a ranking based on the parameters. A first objective function generates a parameter indicating an extent to which a property listing matches preferences of the guest, and is based on data about the guest's interactions with the reservation system. A second objective function generates another parameter indicating an extent to which the search request matches the preferences of the host associate with the property listing, and is based on data about the host's responses to reservation requests. 1. A reservation system for property listings , comprising:memory for storing the property listings; anda machine learning system configured to receive a plurality of search requests submitted by a guest for requesting searches of the property listings and to provide ordered lists of the property listings in response to the search requests, the plurality of search requests including a first search request and the ordered lists including a first ordered list provided by the machine learning system in response to the first search request, the machine learning system configured to receive first data indicative of interactions by the guest with the ordered lists and to receive second data indicative of host responses to reservation requests for the property listings, the host responses from hosts associated with the property listings, wherein the machine learning system is configured to analyze the first data according to a first objective function for training the machine learning system to control rankings of a plurality of the property listings within the first ...

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Номер записи: 8
24-11-2016 дата публикации

ADAPTIVE ADVERTISEMENT TARGETING BASED ON PERFORMANCE OBJECTIVES

Номер: US20160343026A1
Автор: Cheng Haibin, Hong Sue Ann, Kirti Rituraj, Savage Benjamin Tucker, Wu Gary
Принадлежит:

A target audience for an ad campaign is determined during an exploration period of the ad campaign by modifying the target audience based on the fulfillment of performance objectives. An initial target audience may be provided by the advertiser or determined by the social networking system based on ad campaigns having similar ad content or other similar characteristics. Advertisements associated with the ad campaign are served to users of the initial target audience. A subset of the target audience that fulfills the performance objectives of the ad campaign is identified and those users are used to generate a new targeting audience to target users that “look like” the subset of the target audience. The new targeting audience is used in place of the initial target audience to improve targeting for the advertisement. This process may be iteratively performed to refine the target audience during the exploration period. 1. A method comprising:receiving a request for an ad campaign from an advertiser, the request for the ad campaign comprising a first advertisement and one or more performance objectives specifying user actions performed subsequent to receipt of the first advertisement;determining a first initial target audience of users who are eligible to be served the first advertisement;sending the first advertisement for delivery to a first set of users of the first initial target audience of users;receiving performance data about the first advertisement presented to the first set of users of the first initial target audience of users, the performance data indicating which users performed user actions defined by the one or more performance objectives of the ad campaign;identifying a first subset of users of the initial target audience that satisfied the one or more performance objectives of the ad campaign based on the received performance data; anddetermining a first revised target audience for the ad campaign based on the identified first subset of users of the ...

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Номер записи: 9
24-11-2016 дата публикации

CLUSTERING USERS OF A SOCIAL NETWORKING SYSTEM BASED ON USER INTERACTIONS WITH CONTENT ITEMS ASSOCIATED WITH A TOPIC

Номер: US20160343027A1
Автор: Cheng Haibin, Hong Sue Ann, YE Xingyao
Принадлежит:

A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic. 1. A method comprising:identifying users of a social networking system who have previously interacted with an advertisement included in an advertisement request (“ad request”) including a topic in targeting criteria;identifying one or more characteristics associated with each of the users who were presented with the advertisement included in the ad request and who have previously interacted with the advertisement included in the ad request including the topic in targeting criteria;identifying a cluster group including additional users having at least a threshold measure of similarity to one or more of the identified users based at least in part on characteristics of the additional users and the identified one or more characteristics associated with each of the users who were presented with the advertisement included in the ad request and who have previously interacted with the advertisement included in the ad request including the topic in targeting criteria;determining a score for each of the additional users included in the ...

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Номер записи: 10
05-12-2023 дата публикации

Ranking property listing search results

Номер: US11836139B2
Автор: Brendan Marshall Collins, Haibin Cheng, Malay Haldar, Tao Xu
Принадлежит: Airbnb Inc

An online reservation system is configured to receive requests from a guest for searching property listings and to return property listings that satisfy the search criteria of the requests. The online reservation system uses a machine learning system to rank the property listings returned by the search. The machine learning system uses objective functions to determine parameters for each property listing and assign a ranking based on the parameters. A first objective function generates a parameter indicating an extent to which a property listing matches preferences of the guest, and is based on data about the guest's interactions with the reservation system. A second objective function generates another parameter indicating an extent to which the search request matches the preferences of the host associate with the property listing, and is based on data about the host's responses to reservation requests.

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Номер записи: 11
17-10-2019 дата публикации

Ranking property listing search results

Номер: WO2019199843A1
Автор: Brendan Marshall Collins, Haibin Cheng, Malay Haldar, Tao Xu
Принадлежит: AIRBNB, Inc.

An online reservation system (15) is configured to receive requests from a guest for searching property listings (77) and to return property listings that satisfy the search criteria of the requests. The online reservation system uses a machine learning algorithm to rank the property listings returned by the search. The reservation system uses objective functions to determine parameters for each property listing and assign a ranking based on the parameters. A first objective function generates a parameter indicating an extent to which a property listing matches preferences of the guest, and is based on data about the guest's interactions with the reservation system. A second objective function generates another parameter indicating an extent to which the search request matches the preferences of the host associate with the property listing, and is based on data about the host's responses to reservation requests.

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Номер записи: 12