POI recommendation based on LBSN and multi-graph fusion

doi:10.11947/j.AGCS.2022.20210156

Abstract

Abstract: As an important branch of the recommendation field, point of interest (POI) recommendation has always been favored by researchers. This paper proposes a POI recommendation algorithm based on location-based social network (LBSN) and multi-graph fusion, GraphPOI. It comprehensively analyzes the internal factors and external representations of users and POIs. First, it learns from the user-POI rating matrix to obtain the internal latent vector of users and POIs. Then, it constructs a user-POI interaction diagram according to the rating matrix, and obtains the representation vector of the POI in the user space and the representation vector of the user in the POI space. Next,it clusters the POIs according to their geographic locations to obtain the representation vector of the POI in the location space, and combines the representation vector of the POI in the user space to obtain the POI's external representation vector. At the same time, it models the information diffusion phenomenon in the user's social graph, captures the user's friendship to obtain the user's representation vector in the social space, and combines the user's representation vector in the POI space to obtain the user's external representation vector. Last, the internal latent vector and external representation vector of the user and the POI are combined to obtain the final vector representation of the user and the POI, which is input into the multi-layer neural network model for scoring prediction. The proposed model is verified on the Yelp dataset, and the results demonstrate that the method proposed in this paper can effectively improve the accuracy of POI recommendation.

Key words: POI recommendation, location-based social network, clustering, multi-graph fusion, vector representation

CLC Number:

P208

FANG Jinfeng, MENG Xiangfu. POI recommendation based on LBSN and multi-graph fusion[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(5): 739-749.

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