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Multi-relational Topic Model for Social Recommendation

Various attribute and relation information is used in social recommendation systems. However, previous approaches fail to use them in a unified way. In this paper, we propose a unified framework for social recommendation. Entities like users and items are described by their tags. We model each entity using topic models like Latent Dirichlet Allocation(LDA) and then connect these topic models to form a multi-relational network. Various relations between entities in recommender systems such as rating relation or user friend relation can be expressed as edges in the multi-relational network. We evaluate our model on a real-life dataset collected from a commercial recommender website. Experiments validate the generative performance and predictive performance of our model.