Learning to recommend top-k items in online social networks

In this paper, we propose SIR, a Social Item Recommendation model based on latent variable model and neighborhood model which effectively models the user interest similarities and social relationships in online social networks. We develop the learning algorithm for the parameter estimates of SIR. Furthermore, we construct an extended SIR model (SIR+) by taking the social interaction features into account to improve the performance of top-A item recommendation. The experiments on a real dataset from Sina Weibo, one of the most popular social network sites (SNS) in China, demonstrate that both SIR and SIR+ outperform the traditional collaborative filtering methods, and SIR+ achieves a better performance than SIR.