DP-POIRS: A Diversified and Personalized Point-of-Interest Recommendation System
Xiangfu Meng, Yanhuan Tang, Xiaoyan Zhang · 2017
Diversity point-of-interest recommendation system benefits users to broaden their interests, access and discover new interest points. This paper describes a Diversified and Personalized Point-Of-Interest Recommendation System (DP-POIRS) by leveraging the geo-social relationships between POIs. The system consists of three components. The first component - geo-social distance measuring component is used to construct a correlation matrix to describe the geo-social distance between points of interests. The second component -point-of-interest partition component, divides the interest points into diverse clusters by using the spectral clustering algorithm over the correlation matrix. The third component -personalized sorting component, finds out the user's favorite interest points from each cluster, and then sorts them into a list of recommendation by the use of matrix factorization algorithms.