Improving Rating Prediction in Multi-Criteria Recommender Systems Via a Collective Factor Model-Reference-Cited by-同舟云学术

Improving Rating Prediction in Multi-Criteria Recommender Systems Via a Collective Factor Model

Author:

Fan Ge¹^ORCID,Zhang Chaoyun²,Chen Junyang³^ORCID,Li Paul⁴,Li Yingjie¹,Leung Victor C. M.³^ORCID

Affiliation:

1. Lightspeed & Quantum Studios, Tencent Inc., Shenzhen, China

2. Microsoft, Beijing, China

3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

4. Baidu, Sunnyvale, CA, USA

Funder

National Natural Science Foundation of China

Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy

Natural Science Foundation of Guangdong Province of China

Shenzhen Talents Special Project - Guangdong Provincial Innovation and Entrepreneurship Team Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Control and Systems Engineering

Link

Reference63 articles.

4. Predicting ratings in multi-criteria recommender systems via a collective factor model;fan;Proc DeMaL Web Conf,0

5. Collaborative filtering with multi-component rating for recommender systems;sahoo;Proc 16th Workshop Inf Technol Syst,0

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