In recommender systems, personal characteristic is possessed by not only users but also displaying products. Users have their personal rating patterns while products have different characteristics that attract users. This information can be explicitly exploited from the review text. However, most existing methods only model the review text as a topic preference of products, without considering the perspectives of users and products simultaneously. In this paper, we propose a user-product topic model to capture both user preferences and attractive characteristics of products. Different from conventional collaborative filtering in conjunction with topic models, we use non-negative matrix tri-factorization to jointly reveal the characteristic of users and products. Experiments on two real-world data sets validate the effectiveness of our method in Top-N recommendations.
|Title of host publication||Database Systems for Advanced Applications : 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, proceedings, part I|
|Editors||Jian PEI, Yannis MANOLOPOULOS, Shazia SADIQ, Jianxin LI|
|Publisher||Springer International Publishing AG|
|Number of pages||17|
|Publication status||Published - 2018|
|Event||23rd International Conference on Database Systems for Advanced Applications - Gold Coast, Australia|
Duration: 21 May 2018 → 24 May 2018
|Name||Lecture Notes in Computer Science|
|Conference||23rd International Conference on Database Systems for Advanced Applications|
|Abbreviated title||DASFAA 2018|
|Period||21/05/18 → 24/05/18|
Bibliographical noteWe are grateful to the anonymous reviewers for their valuable comments on this manuscript. The research has been supported by the National Natural Science Foundation of China (61502545, U1611264, U1711262), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Individual Research Scheme of the Dean’s Research Fund 2017–2018 (FLASS/DRF/IRS-8) of The Education University of Hong Kong.
- Top-N recommender system
- Topic model
- Matrix tri-factorization