AUTHOR=Acharya Malika , Mohbey Krishna Kumar 

TITLE=Recency-based spatio-temporal similarity exploration for POI recommendation in location-based social networks

JOURNAL=Frontiers in Sustainable Cities

VOLUME=Volume 6 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2024.1331642

DOI=10.3389/frsc.2024.1331642

ISSN=2624-9634

ABSTRACT=Point-of-Interest (POI) recommendation is one of the primary tasks of Location-based Social   Networks (LBSNs). With user data in bulk, extracting useful information and addressing issues like data sparsity and cold-start problems looming large in collaborative filtering becomes difficult.One of the plausible solutions is to combine contextual information into the recommendation process. In this paper, we propose a Recency-based Spatio-Temporal Similarity Exploration (RSTSE) for POI Recommendation that utilizes the recency-based trust estimation among the prospective neighbors of the target user. The trust level is categorized into two heads: direct trust, which can be extracted from the peer group information of the user, and indirect trust, which is measured based on the venue popularity, temporal recency, radial proximity, and transitivity. The approach consists of two phases. In the incipient phase, POIs of interest are extracted based on the preferences of potential neighbors, including the users who are recognized peers, the users with similar visiting histories in the spatial and temporal context, and the users with friend-of-friend relations. The telic phase involves the Neural Collaborative Filtering to capture the linear and non-linear user-POI interactions better. RSTSE has been evaluated on three real-world datasets, namely, Gowalla. Foursquare and Weeplaces and the results suggest the efficacy over other state-of-the-art approaches.