Context-aware collaborative topic regression with social matrix factorization for recommender systems

Chaochao Chen*, Xiaolin Zheng, Yan Wang, Fuxing Hong, Zhen Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

57 Citations (Scopus)

Abstract

Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.

Original languageEnglish
Title of host publicationProceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
Place of PublicationPalo Alto, California
PublisherAI Access Foundation
Pages9-15
Number of pages7
Volume1
ISBN (Electronic)9781577356776
Publication statusPublished - 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN (Print)2159-5399

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period27/07/1431/07/14

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  • Cite this

    Chen, C., Zheng, X., Wang, Y., Hong, F., & Lin, Z. (2014). Context-aware collaborative topic regression with social matrix factorization for recommender systems. In Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence (Vol. 1, pp. 9-15). (Proceedings of the AAAI Conference on Artificial Intelligence). Palo Alto, California: AI Access Foundation.