Redefining POI Popularity: Integrating User Preferences and Recency for Enhanced Recommendations

TL;DR

This paper introduces a recency-oriented POI popularity model that incorporates temporal effects, check-in counts, and user diversity to improve point-of-interest recommendations.

Contribution

It proposes a novel popularity measure emphasizing recency and user diversity, enhancing POI recommendation accuracy over traditional check-in count methods.

Findings

Recency-oriented popularity improves recommendation relevance.

Incorporating user diversity enhances POI ranking.

Experimental results validate the effectiveness of the proposed model.

Abstract

The task of point-of-interest (POI) recommendation is to predict users' immediate future movements based on their previous records and present circumstances. Popularity is considered as one of the primary deciding factors for selecting the next place to visit. Existing approaches mainly focused on the number of check-ins to model the popularity of a POI. However, not enough attention is paid to the temporal impact or number of people check-ins for a particular POI. Thus, to prioritize more on recent check-ins, we propose recency-oriented definition of POI's popularity by considering the temporal effect of the POIs, the number of check-ins, as well as the number of users who registered in those check-ins. Our experimental results on real dataset show the efficacy of the proposed approach.