Affiliations: [a] School of Computer Science and Technology, Anhui University, Hefei 230601, Auhui, China | [b] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, Anhui, China | [c] School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504-3694, USA
Correspondence:
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Corresponding author: Liangquan Yang, School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, China. Tel.: +86 15395602392; E-mail: [email protected].
Abstract: Recently how to recommend celebrities to public has become an interesting problem in real social network applications. In this problem, a matrix of users’ following actions is usually very sparse. Therefore, it causes conventional collaborative filtering based methods to degrade significantly in recommendation performance. To address the sparsity problem, side information could be rendered. Collaborative social topic regression (CSTR) is an appealing new method, which combines the matrix of general users’ following actions, content information and a social network of celebrities. However, this method is limited by using the topic model latent Dirichlet allocation (LDA) as the critical component. The learned content representation may not be compact and effective enough. Moreover, the social network of general users also exists, which is helpful for recommendation. In this paper, we employ a deep learning component to learn more effective feature representation and incorporate social network information of general users by adding social regularization terms. We propose a novel hierarchical Bayesian model named collaborative social deep learning (CSDL), which jointly handles deep learning for the content information and collaborative filtering for general users’ following actions, the social network of celebrities and that of general users. Experiments on two real-world datasets show the effectiveness of our proposed model.
Keywords: Celebrity recommendation, deep learning, social network, text mining
