Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources-Reference-Cited by-同舟云学术

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

Published:2017-11-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
language:
Short-container-title:

Author:

Zhang Yongfeng¹,Ai Qingyao¹,Chen Xu²,Croft W. Bruce¹

Affiliation:

1. University of Massachusetts Amherst, Amherst, MA, USA

2. Tsinghua University, Beijing, China

Funder

National Science Foundation

Center for Intelligent Information Retrieval

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3132847.3132892

Reference46 articles.

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3. Yang Bao Hui Fang and Jie Zhang. 2014. TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation. AAAI. Yang Bao Hui Fang and Jie Zhang. 2014. TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation. AAAI.

4. Konstantin Bauman Bing Liu and Alexander Tuzhilin. 2017. Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews. In KDD. 717--725. 10.1145/3097983.3098170 Konstantin Bauman Bing Liu and Alexander Tuzhilin. 2017. Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews. In KDD. 717--725. 10.1145/3097983.3098170

5. Representation Learning: A Review and New Perspectives

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