关键词: 潜在的狄利克雷分配（LDA）主题模型, 动态预测, 兴趣点推荐, 相似度

Abstract: There are two problems in the existing POI recommendation algorithm. First, most algorithms mainly utilize the historical check-in data of user, while ignoring the comments of users and label information. Thus, the cold-start problem cannot be solved effectively. Second, some algorithms only use the user’s check-in score when calculating the similarity. The high sparseness of the POI check-in matrix results in the inaccurate ness of the recommendation. In view of the above problems, this paper uses the LDA topic model to mine the user’s interest topic, and then integrates the check-in data for similarity measure to solve the cold-start problem. In recommendation period, a dynamic prediction method is proposed to dynamically fill the missing data and further alleviate the sparse data and improve the recommended quality. The experimental results on the real data set show that the proposed similarity fusion and dynamic prediction based recommend algorithm can effectively solve the problem of data sparseness and cold-start. The recommend performance is superior to traditional recommendation algorithms.

Key words: Latent Dirichlet Allocation（LDA） topic model, dynamic prediction, point of interest recommendation, similarity