Collaborative topic regression with social regularization for tag recommendation

Hao Wang, Binyi Chen, Wu Jun Li

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

105 Scopus citations

Abstract

Recently, tag recommendation (TR) has become a very hot research topic in data mining and related areas. However, neither co-occurrence based methods which only use the item-tag matrix nor content based methods which only use the item content information can achieve satisfactory performance in real TR applications. Hence, how to effectively combine the item-tag matrix, item content information, and other auxiliary information into the same recommendation framework is the key challenge for TR. In this paper, we first adapt the collaborative topic regression (CTR) model, which has been successfully applied for article recommendation, to combine both item-tag matrix and item content information for TR. Furthermore, by extending CTR we propose a novel hierarchical Bayesian model, called CTR with social regularization (CTR-SR), to seamlessly integrate the item-tag matrix, item content information, and social networks between items into the same principled model. Experiments on real data demonstrate the effectiveness of our proposed models.

Original languageEnglish (US)
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages2719-2725
Number of pages7
StatePublished - 2013
Externally publishedYes
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
Country/TerritoryChina
CityBeijing
Period8/3/138/9/13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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