关键词: 相关主题模型（CTM）, 概率矩阵分解（PMF）模型, 用户兴趣因子, 物品推荐

Abstract: In order to overcome the shortcomings of the traditional collaborative filtering method, a new recommendation method based on CTM-PMF（Correlated Topic Model and Probabilistic Matrix Factorization） model is proposed. CTM-PMF model combines the PMF model in collaborative filtering and the CTM model in topic modes. The result of this combination is that both advantages of PMF model and advantages of CTM model are combined, solves the problem of new items. In this method, a factor is also introduced which reflects the change of user’s interest on the products bought previously. The algorithm is tested on a self-build product dataset. The experimental results show that the new method has better capacity for recommendation compared to PMF, G-PLSA and UBCF method.

Key words: Correlated Topic Model（CTM）, Probabilistic Matrix Factorization（PMF） model, user interest factor, product recommendation