Variational Deep Collaborative Matrix Factorization for Social Recommendation

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Author(s)
Xiao, Teng
Tian, Hui
Shen, Hong
Griffith University Author(s)
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2019
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Abstract

In this paper, we propose a Variational Deep Collaborative Matrix Factorization (VDCMF) algorithm for social recommendation that infers latent factors more effectively than existing methods by incorporating users’ social trust information and items’ content information into a unified generative framework. Unlike neural network-based algorithms, our model is not only effective in capturing the non-linearity among correlated variables but also powerful in predicting missing values under the robust collaborative inference. Specifically, we use variational auto-encoder to extract the latent representations of content and then incorporate them into traditional social trust factorization. We propose an efficient expectation-maximization inference algorithm to learn the model’s parameters and approximate the posteriors of latent factors. Experiments on two sparse datasets show that our VDCMF significantly outperforms major state-of-the-art CF methods for recommendation accuracy on common metrics.

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Lecture Notes in Computer Science
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Subject
Theory of computation
Information systems
Information retrieval and web search
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Citation
Xiao, T; Tian, H; Shen, H, Variational Deep Collaborative Matrix Factorization for Social Recommendation, Advances in Knowledge Discovery and Data Mining , 2019, pp. 426-437