关键词: 兴趣点推荐, 泊松分解, 神经网络, 贝叶斯个性化排序

Abstract:

The implicit feedback modeling user-Point-of-Interest（POI）interaction accuracy of the POI recommendation system is not high and the implicit feedback attribute of the use’s check-in data is ignored. A novel POI recommendation algorithm is proposed. Specifically, first of all, a neural network-based ranking algorithm is used to capture the interaction relationship of user-POI. Then, the Poisson factorization algorithm and Bayesian personalized ranking technology are combined to model the user’s check-in behavior. The algorithms obtained in the above two steps are integrated into a unified recommendation algorithm architecture to provide a POI recommendation service. The experimental results show that the proposed algorithm is better than the traditional state-of-the-art POI recommendation algorithm.

Key words: Point-of-Interest（POI） recommendation, Poisson factorization, neural network, Bayesian personalized ranking