APPLYING A TRAINED MODEL FOR PREDICTING QUALITY OF A CONTENT ITEM ALONG A GRADUATED SCALE

    公开(公告)号:US20190095961A1

    公开(公告)日:2019-03-28

    申请号:US15726114

    申请日:2017-10-05

    Applicant: Facebook, Inc.

    Abstract: An online system receives a request to present a content item to a viewing user who is associated with a set of user attributes. The online system retrieves a regression model for predicting an expected quality for a particular content item and a particular set of users attributes. The regression model was trained, using machine learning, based on user-assigned quality scores, each corresponding to a content item and provided by a quality-assigning user, and sets of user attributes, each set associated with one of the quality-assigning users. The online system uses the regression model to predict a quality score, indicating the quality of a content item to the viewing user, based on the set of user attributes that is associated with the viewing user. The online system determines to provide the content to the viewing user based on the quality score, and transmits the content item to the viewing user.

    OPTIMIZING USER ENGAGEMENT WITH CONTENT BASED ON AN OPTIMAL SET OF ATTRIBUTES FOR MEDIA INCLUDED IN THE CONTENT

    公开(公告)号:US20200042610A1

    公开(公告)日:2020-02-06

    申请号:US16051486

    申请日:2018-08-01

    Applicant: Facebook, Inc.

    Abstract: An online system identifies a candidate content item eligible for presentation to a viewing user of the online system, in which the candidate content item includes media (e.g., an image, a video, etc.). The online system identifies one or more media attributes for the media, such as color saturation, tone, brightness, sharpness, contrast, etc. The online system also predicts a value of a performance metric for the candidate content item that indicates a likelihood of user engagement with the candidate content item by the viewing user. For each modification that may be made to a media attribute, the online system predicts a change to the value of the performance metric. Based on the predicted change, the online system determines an optimal set of media attributes associated with a maximum predicted value of the performance metric. The online system modifies the media based on the optimal set of media attributes.

Patent Agency Ranking