A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

Abstract

User-POI rating matrix is one of the current research hotspot of POI recommendation algorithms, the goal of which is to obtain the POIs with the highest user satisfaction. However, most existing POI recommendation algorithms ignore the diversity of recommended POI list, and the POIs in the recommended list are usually similar to each other, which cannot effectively broad the user’s perspectives. To solve this problem, a diverse and personalized recommendation method EBPRMF (Embedded Bayesian Personalized Ranking Matrix Factorization) that integrates the embedded features of the geographic-social relationships of POIs is proposed. First, by embedding and compressing the geographic and social relationships between POIs, a geographic-social relationship embedding model is constructed to evaluate the coupled correlation between POIs. The correlation between all POIs forms a POI correlation matrix. And then the spectral clustering method is leveraged to cluster the POIs according to the correlation matrix, and several different POI clusters can be obtained. Next, the Bayesian personalized ranking matrix factorization model is proposed to select POI that is close to the user’s preference from each cluster, and finally a recommendation list of POIs that is both diverse and personalized is obtained. Experimental results demonstrate that the geographic-social relationship embedding model can well represent the location and social relationship between POIs, and has a good clustering effect. The diversity of recommendation list is high, and the sorted list can be well satisfied with user preferences.