Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

Abstract

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.

Submitted in Jul 2020 (revised in September 2021, accepted in January 2022, online January 2022)

References in corpus (2)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• A Survey of Point-of-interest Recommendation in Location-based Social Networks

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• CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
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• Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms
• DAGFiNN: A Conversational Conference Assistant
• Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
• Point of Interest Recommendation: Pitfalls and Viable Solutions