公开(公告)号：US10909454B2

公开(公告)日：2021-02-02

申请号：US15469550

申请日：2017-03-26

Applicant: Facebook, Inc.

Inventor： Shilin Ding ,  Min Li ,  Liang Xiong

IPC: G06N3/08 ,  G06F16/95

Abstract: For a content item with unknown tasks performed by a viewing user on an online system, the online system receives a plurality of content items associated with a viewing user. The online system derives a feature vector for each content item. The online system predicts a likelihood of interacting with each content item using a prediction model associated with a plurality of tasks. The prediction model comprises a plurality of shared layers and a plurality of separate layers. The plurality of shared layers are configured to extract common features that are shared across the plurality of tasks. Each separate layer is configured to predict likelihood of the viewing user performing a task associated with the separate layer based on the common features. The online system scores each content item based on predicted likelihood of each task. The online system ranks the plurality of content items based on the scoring.

公开(公告)号：US20180276533A1

公开(公告)日：2018-09-27

申请号：US15469550

申请日：2017-03-26

Applicant: Facebook, Inc.

Inventor： Shilin Ding ,  Min Li ,  Liang Xiong

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/08 ,  G06F16/95

Abstract: For a content item with unknown tasks performed by a viewing user on an online system, the online system receives a plurality of content items associated with a viewing user. The online system derives a feature vector for each content item. The online system predicts a likelihood of interacting with each content item using a prediction model associated with a plurality of tasks. The prediction model comprises a plurality of shared layers and a plurality of separate layers. The plurality of shared layers are configured to extract common features that are shared across the plurality of tasks. Each separate layer is configured to predict likelihood of the viewing user performing a task associated with the separate layer based on the common features. The online system scores each content item based on predicted likelihood of each task. The online system ranks the plurality of content items based on the scoring.