A novel collective matrix factorization model for recommendation with fine‐grained social trust prediction-Reference-Cited by-同舟云学术

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A novel collective matrix factorization model for recommendation with fine‐grained social trust prediction

Published:2017-07-28 Issue:19 Volume:29 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat: Pract Exper

Author:

Wang Jinkun¹,Zhang Shasha²,Liu Xiao³^ORCID,Jiang Yuanchun¹,Zhang Min²

Affiliation:

1. School of Management Hefei University of Technology Hefei 230009 Anhui China

2. Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai 200062 China

3. School of Information Technology Deakin University Melbourne Vic 3125 Australia

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.4233

Reference55 articles.

1. MaH LyuMR KingI.Learning to recommend with trust and distrust relationships. Paper presented at: Proceedings of the Third ACM Conference on Recommender Systems.New York New York USA:ACM;2009.

2. O'DonovanJ SmythB.Trust in recommender systems. Paper presented at: Proceedings of the 10th International Conference on Intelligent User Interfaces.San Diego California USA:ACM;2005.

3. Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation

4. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

5. KorenY.Factorization meets the neighborhood: A multifaceted collaborative filtering model. Paper presented at: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Diego California USA:ACM;2008.

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