公开(公告)号：US10565518B2

公开(公告)日：2020-02-18

申请号：US14748059

申请日：2015-06-23

Applicant: Adobe Inc.

Inventor： Hailin Jin ,  Chen Fang ,  Jianchao Yang ,  Zhe Lin

IPC: G06N20/00 ,  G06N3/02 ,  H04L29/08

Abstract: The present disclosure is directed to collaborative feature learning using social media data. For example, a machine learning system may identify social media data that includes user behavioral data, which indicates user interactions with content item. Using the identified social user behavioral data, the machine learning system may determine latent representations from the content items. In some embodiments, the machine learning system may train a machine-learning model based on the latent representations. Further, the machine learning system may extract features of the content item from the trained machine-learning model.

公开(公告)号：US11042798B2

公开(公告)日：2021-06-22

申请号：US15082877

申请日：2016-03-28

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Jianchao Yang ,  Hailin Jin ,  Chen Fang

IPC: G06N3/08 ,  G06N5/04 ,  G06N20/00 ,  G06F16/532 ,  G06F16/58 ,  G06N3/04

Abstract: Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.