@inproceedings{7ef3c40ddcc346a1a660410da5b09010,
title = "A synthetic approach for recommendation: Combining ratings, social relations, and reviews",
abstract = "Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.",
author = "Hu, {Guang Neng} and Dai, {Xin Yu} and Celine SONG and Huang, {Shu Jian} and Chen, {Jia Jun}",
note = "Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; 24th International Joint Conference on Artificial Intelligence, IJCAI 2015 ; Conference date: 25-07-2015 Through 31-07-2015",
year = "2015",
month = jul,
language = "English",
isbn = "9781577357384",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "AAAI press",
pages = "1756--1762",
editor = "Michael Wooldridge and Qiang Yang",
booktitle = "IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence",
}