关键词: 时间因素, 隐语义模型, 聚类算法, 个性化推荐

Abstract: With the development of personalized recommendation technologies, recommender systems are facing increasingly more challenges. Traditional recommendation algorithms usually have the problems of data sparsity and low recommendation accuracy. To solve the above problems, this paper proposes a recommendation algorithm [K]-TLFM（Time Based Latent Factor Model Integrated with [k]-means）, which combines time effect based latent semantic model with clustering methods for subgroup partitioning. Firstly, the missing items of the original rating matrix are filled by the proposed time based latent semantics model. This alleviates the errors caused by the filling with global average or user/item average, and effectively alleviates the problem of data sparsity. Meanwhile, the fusion of the time factor efficiently depicts the variations of user preferences over time. Secondly, after the completion of the missing items in the scoring matrix, items with similar preferences and interest characteristics are partitioned into the same clusters based on the bisecting [k]-means algorithm. There commendation lists are generated for targeted users by the selected collaborative filtering algorithms according to their subgroups. Lastly, this paper carries out several experiments to test the performance of the proposed [K]-TLFM with other state-of-the-art recommendation algorithms on the datasets of MovieLens and Netflix. The experimental results show both the efficiency and higher recommendation accuracy of the proposed algorithm.

Key words: time factor, latent factor model, clustering algorithms, personalized recommendation