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公开(公告)号：US10803094B1
公开(公告)日：2020-10-13
申请号：US15883028
申请日：2018-01-29
Applicant: Facebook, Inc.
Inventor： Chaochao Cai , Goran Predovic
IPC: G06F16/00 , G06F16/28 , G06F16/955
Abstract: A method for determining reach of a content item that is displayed on one or more client devices associated with at least one unresolved identifier. An unresolved identifier defines a context in which a client device accesses one or more online systems, the context not determined to be associated with a specific user. The method comprises identifying a set of unresolved identifiers, and identifying information describing one or more access events associated with each unresolved identifier. For each pair of unresolved identifiers, a similarity score for the pair is determined based on the identified information. Responsive to the similarity score exceeding a threshold similarity score, the pair of unresolved identifiers is clustered, the clustering indicating a prediction that the pair of unresolved identifiers are associated with a common user. Finally, for the reach of the displayed content item is determined based on the clustering of the set of unresolved identifiers.
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公开(公告)号：US10691930B1
公开(公告)日：2020-06-23
申请号：US16506859
申请日：2019-07-09
Applicant: Facebook, Inc.
Inventor： Aleksey Sergeyevich Fadeev , Li Zhou , Yimin Song , Goran Predovic , Chaochao Cai , Liang Xu
Abstract: An online system matches a user across multiple online systems based on image data for the user (e.g., profile photo) regardless whether the image data is from the online system, a different but related online system or a third party system. For example, to match the user across a social networking system and INSTAGRAM™ system, the online system compares the similarity between images of the user from both systems in addition to similarity of textual information in the user profiles on both systems. The similarity of image data and the similarity of textual information associated with the user are used by the online system as indicators of matched user accounts belonging to the same user across both systems. The online system applies models trained using deep learning techniques to match a user across multiple online systems based on the image data and textual information associated with the user.
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公开(公告)号：US20180189676A1
公开(公告)日：2018-07-05
申请号：US15397530
申请日：2017-01-03
Applicant: Facebook, Inc.
Inventor： Goran Predovic , Chaochao Cai
CPC classification number: G06N20/00 , G06F16/00 , G06F16/9535 , G06Q10/10 , G06Q30/0241 , G06Q30/0251 , G06Q30/0269 , G06Q50/01
Abstract: Disclosed is an online system that infers interests of unresolved users for whom the interests are not known. The online system determines certain features about the unresolved users, but does not have certain information about the users themselves (e.g., their interests), so instead infers these attributes based on the features of the user. The online system provides the features as input to a classifier trained to predict a particular interest, and the classifier outputs a prediction of whether the user has the corresponding interest. In one embodiment, the online system trains a classifier for various interest values by forming training sets for the interests using the features for users who are logged into the online system and hence have known interests.
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公开(公告)号：US20170213241A1
公开(公告)日：2017-07-27
申请号：US15007125
申请日：2016-01-26
Applicant: Facebook, Inc.
Inventor： Li Zhou , Goran Predovic , Ovidiu Popa , Vikram Mukunda Rao Tankasali , Liang Xu
CPC classification number: G06Q30/0246 , G06Q30/0201 , G06Q50/01
Abstract: An audience analysis system determines and predicts reach and frequency information of online users. The system receives real-time ad impression data from ad publishers or other data providers as well as report requests from advertisers asking for the reach and frequency information. The reach and frequency information of online users describes characteristics of online users that are reached by the advertisers. Matched users and unmatched users are identified via online cookies. Atomic data units are generated to allow feature computation and reach prediction for online users in a more efficient way. Machine learning models are trained to help predict the reach and frequency of unmatched users and to generate reports. The audience analysis system provides the advertisers with the generated reports, responding to the report requests.
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公开(公告)号：US11144954B1
公开(公告)日：2021-10-12
申请号：US15880078
申请日：2018-01-25
Applicant: Facebook, Inc.
Inventor： Liang Xu , Chaochao Cai , Qing Li , Goran Predovic
Abstract: An online system promotes physical store visits by presenting users with content items for a physical store location and subsequently logs visits of online system users to the physical store location to track performance of a campaign associated with the presented content item. The online system registers attention events associated with the presented content items presented to users on third party publishing sites via tracking pixels and registers attention events as store front visit conversion events if, within a predetermined period of time from a valid attention event, a user has subsequently gone in and visited the physical store front location.
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Patent Agency Ranking
公开(公告)号：US11144953B1
公开(公告)日：2021-10-12
申请号：US15956462
申请日：2018-04-18
Applicant: Facebook, Inc.
Inventor： Jie Zhang , Pu Zhang , Ying Jiang , Sergiy Dubynskiy , Goran Predovic
Abstract: A user action associated with a content item performed by a target user is detected. Information describing online publishers that delivered the content item to the target user is retrieved. For each publisher, a likelihood that the user action would have occurred without the publisher's delivery of the content item to the target user is determined. An estimated increase in the likelihood that the user action occurred due to the publisher's delivery of the content item to the target user is determined. A baseline value indicating a likelihood that the user action would have occurred without delivery of the content item to the target user by any publishers is estimated based on attributes for the target user. A performance metric is determined for each publisher, wherein ratios of the metrics are scaled based on the baseline value and are related based on corresponding ratios of the estimated increases in likelihoods.
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公开(公告)号：US10554721B2
公开(公告)日：2020-02-04
申请号：US14866059
申请日：2015-09-25
Applicant: Facebook, Inc.
Inventor： Goran Predovic , Liang Xu , Stephane Taine
Abstract: An online system determines one or more metrics describing consumption of content by various users by identifying users of the online system capable of being identified based on information received from multiple client devices. For example, the online system identifies users associated with user identifiers that are also associated with other types of identifying information (e.g., cookies, device identifiers). From the identified users, the online system generates a set of users based on a distribution of characteristics. The distribution of characteristics may be determined by the online system as characteristics of a group of users or received by the online system from a third party system and describes characteristics of users of the third party system. Based on interactions with content by users in the set, the online system determines one or more metrics describing consumption of content.
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公开(公告)号：US10242251B2
公开(公告)日：2019-03-26
申请号：US15497454
申请日：2017-04-26
Applicant: Facebook, Inc.
Inventor： Aleksey Sergeyevich Fadeev , Li Zhou , Yimin Song , Goran Predovic , Chaochao Cai , Liang Xu
Abstract: An online system matches a user across multiple online systems based on image data for the user (e.g., profile photo) regardless whether the image data is from the online system, a different but related online system or a third party system. For example, to match the user across a social networking system and INSTAGRAM™ system, the online system compares the similarity between images of the user from both systems in addition to similarity of textual information in the user profiles on both systems. The similarity of image data and the similarity of textual information associated with the user are used by the online system as indicators of matched user accounts belonging to the same user across both systems. The online system applies models trained using deep learning techniques to match a user across multiple online systems based on the image data and textual information associated with the user.
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公开(公告)号：US10832167B2
公开(公告)日：2020-11-10
申请号：US15397530
申请日：2017-01-03
Applicant: Facebook, Inc.
Inventor： Goran Predovic , Chaochao Cai
IPC: G06K9/62 , G06Q30/02 , G06K9/00 , H04N21/25 , G06F17/00 , G06N20/00 , G06Q50/00 , G06Q10/10 , G06F16/00 , G06F16/9535
Abstract: Disclosed is an online system that infers interests of unresolved users for whom the interests are not known. The online system determines certain features about the unresolved users, but does not have certain information about the users themselves (e.g., their interests), so instead infers these attributes based on the features of the user. The online system provides the features as input to a classifier trained to predict a particular interest, and the classifier outputs a prediction of whether the user has the corresponding interest. In one embodiment, the online system trains a classifier for various interest values by forming training sets for the interests using the features for users who are logged into the online system and hence have known interests.
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公开(公告)号：US20180314915A1
公开(公告)日：2018-11-01
申请号：US15497866
申请日：2017-04-26
Applicant: Facebook, Inc.
Inventor： Chaochao Cai , Goran Predovic , Wei Wei , Chang Liu , Liang Xu
CPC classification number: G06N5/04 , G06K9/00677 , G06K9/6293 , G06K2009/00322 , G06N99/005
Abstract: An online system predicts gender, age, interests, or other demographic information of a user based on image data of the user, e.g., profile photos, photos the user posts of him/herself within an online system, and photos of the user posted by other users socially connected with the user, and textual data in the user's profile that suggests age or gender (e.g., like or dislikes similar to a population of users of an online system). The online system similarly predicts a user's interests based on the photos of the user. The online system applies one or more models trained using deep learning techniques to generate the predictions. The online system uses the predictions to build more information about the user in the online system, and provide improved and targeted content delivery to the user that may have disparate information scattered throughout different online systems.
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