{"type":"ScholarlyArticle","title":"Collaborative Topic Regression with Social Regularization for Tag Recommendation","authors":[{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering , Shanghai Jiao Tong University , China"},{"name":"Binyi Chen","slug":"binyi-chen","given":"Binyi","family":"Chen","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering , Shanghai Jiao Tong University , China"},{"name":"Wu-Jun Li","slug":"wu-jun-li","given":"Wu-Jun","family":"Li","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering , Shanghai Jiao Tong University , China"}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2013/400","pdf":"https://ijcai.org/proceedings/2013/0400.pdf","markdown":"https://ijcai.org/proceedings/2013/400.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/400","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":400,"pages":{"first":2719,"last":2724},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Adomavicius and Tuzhilin, 2005] G. Adomavicius and A. Tuzhilin. 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