Social regularized von Mises–Fisher mixture model for item recommendation

Collaborative filtering (CF) is a widely used technique to guide the users of web applications towards items that might interest them. CF approaches are severely challenged by the characteristics of user-item preference matrices, which are often high dimensional and extremely sparse. Recently, several works have shown that incorporating information from social networks—such as friendship and trust relationships—into traditional CF alleviates the sparsity related issues and yields a better recommendation quality, in most cases. More interestingly, even with comparable performances, social-based CF is more beneficial than traditional CF; the former makes it possible to provide recommendations for cold start users. In this paper, we propose a novel model that leverages information from social networks to improve recommendations. While existing social CF models are based on popular modelling assumptions such as Gaussian or Multinomial, our model builds on the von Mises–Fisher assumption which turns out to be more adequate, than the aforementioned assumptions, for high dimensional sparse data. Setting the estimate of the model parameters under the maximum likelihood approach, we derive a scalable learning algorithm for analyzing data with our model. Empirical results on several real-world datasets provide strong support for the advantages of the proposed model.