LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation

TL;DR

This paper introduces LGLMF, a novel POI recommendation model that integrates local geographical information into logistic matrix factorization, effectively addressing data scarcity and geographical influence to enhance recommendation accuracy.

Contribution

It proposes a local geographical model that captures user activity regions and location relevance, integrating it into matrix factorization for improved POI recommendations.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively leverages geographical information to improve accuracy

Addresses data scarcity issues in POI recommendation

Abstract

With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix factorization methods provide effective models which can be used in POI recommendation. However, there are two main challenges which should be addressed to improve the performance of POI recommendation methods. First, leveraging geographical information to capture both the user's personal, geographic profile and a location's geographic popularity. Second, incorporating the geographical model into the matrix factorization approaches. To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view. To this end, an effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region. Then, the proposed local geographical model is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation. Experimental results on two well-known datasets demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.