Invention Grant
- Patent Title: Regularized iterative collaborative feature learning from web and user behavior data
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Application No.: US15082877Application Date: 2016-03-28
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Publication No.: US11042798B2Publication Date: 2021-06-22
- Inventor: Zhe Lin , Jianchao Yang , Hailin Jin , Chen Fang
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Kilatrick Townsend & Stockton LLP
- Main IPC: G06N3/08
- 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.
Public/Granted literature
- US20170228659A1 Regularized Iterative Collaborative Feature Learning From Web and User Behavior Data Public/Granted day:2017-08-10
Information query