PREDICTING A USER QUALITY RATING FOR A CONTENT ITEM ELIGIBLE TO BE PRESENTED TO A VIEWING USER OF AN ONLINE SYSTEM

    公开(公告)号:US20180082331A1

    公开(公告)日:2018-03-22

    申请号:US15272764

    申请日:2016-09-22

    Applicant: Facebook, Inc.

    CPC classification number: G06Q30/0255 G06Q30/0269 G06Q30/0273

    Abstract: An online system selects content items for presentation to viewing users of the online system based on a composite score associated with each content item that includes a quality component and a revenue component. The revenue component is based on a monetary amount an advertiser associated with the content item is willing to pay for each interaction with the content item by a prospective viewing user, while the quality component indicates the quality of the content item to the prospective viewing user. The quality component is predicted based on explicit user quality ratings received from viewing users for various content items previously presented to the viewing users, in which the viewing users have at least a threshold measure of similarity to the prospective viewing user and/or the various content items rated by the viewing users have at least a threshold measure of similarity to the content item being scored.

    ADVERTISEMENT RELEVANCE SCORE USING SOCIAL SIGNALS

    公开(公告)号:US20170186029A1

    公开(公告)日:2017-06-29

    申请号:US14983449

    申请日:2015-12-29

    Applicant: Facebook, Inc.

    CPC classification number: G06Q30/0243 G06Q30/0275 G06Q50/01

    Abstract: An online system, such as a social networking system, displays a plurality of advertisements to users. The system selects an ad to display to a user based on a bidding system. The system receives feedback and user engagement data for an ad to compare the ad to other ads that are targeted to a similar group of users, to generate a relevance score. The relevance score can be provided to an advertiser as a way to quantify the effectiveness of the ad, and it reflects user engagement with the advertisement. In some embodiments, a projected relevance score can be calculated for a prospective advertisement by analyzing the content of the prospective ad prior to receiving user engagement data by comparing the prospective advertisement's content to other ads for which user engagement data does exist.

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