Recommender System is an important tool for users to discovery information of their interest, it is also an important tool to overcome information overload problem. The main idea of recommender system is to make recommendation by analyzing users’ historical behaviors. Early researches on recommender system always neglect temporal information, and most of them are focused on the analysis of users’ static behaviors. In recent years, because of Netflix Prize, more and more data sets including temporal information are released, and more and more researchers are studying on temporal recommendation problem. However, there are many problems left in this research area. This paper investigate the temporal recommendation problem by analyzing many public released data sets. Following are main contributions of this paper: 1 Temporal recommendation for rating prediction problem: Rating prediction problem is the most famous problem in recommender system, its main task is to predict a given user’s rating on a given item by analyzing her historical rating on other items. In this paper, we incorporate temporal information into this problem, and propose a latent factor model to model four different temporal effects. Furthermore, we also proposed a cascade model to model seasonal effects. Experimental results show that our method can achieve higher accuracy in rating prediction problem than non-temporal methods. 2 Temporal recommendation for top-N recommendation problem: Top-N recommendation problem is the most important problem in real recommender system, its main task is to recommend N items to every user which will be of their interests by analyzing users’ historical behaviors. In this paper, we introduce a new type of node, session node, into user-item bipartite graph to model users’ long term and short term interests. Furthermore, we also proposed a new graph-based personal ranking method called PathFusion to make recommendation by the new graph model. Experimental results show that our method can make higher accuracy than non-temporal methods and other temporal recommendation methods in top-N recommendation problem. 3 Influence of system update rate on recommender system: User behavior is influenced by social factor and personal factor. However, in the websites with different update rates, these two factors will have different influence. In fast updating sites, users are more influenced by social factor while in slow updating sites, users are more influenced b...
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