Where Shall We Go: Point-of-Interest Group Recommendation With User Preference Embedding

Abstract

In the context of geo-social networks, the objective of Point-of-Interest (POI) group recommendation is to propose POIs that align with the preferences of all members within a specific temporal group. POI group recommendation is significant in enhancing user experience, promoting social interaction, and providing convenient access to information. It also aids in community building and business promotion in real-life scenarios. However, existing studies fail to capture user preferences accurately and reach consensus with respect to preferences for POIs, which leads to the recommendation of POIs with low accuracy. To tackle this issue, we propose a Point-of-Interest (POI) group recommendation model, named PGR-PM, leveraging user preference embedding. Specifically, we first propose a strategy for representing user preferences dynamically by means of POI embedding. Subsequently, we propose a hybrid weight fusion strategy that utilizes an attention mechanism to aggregate the preferences of members within a temporal group. Furthermore, we design a three-layer perceptron structure to recommend POIs for the group. Finally, we conduct comprehensive experiments across four extensively employed real-world datasets, with the findings affirming the efficacy of our proposed approach.