A Survey of Point-of-interest Recommendation in Location-based Social Networks

TL;DR

This survey comprehensively reviews point-of-interest recommendation in location-based social networks, discussing influential factors, methodologies, tasks, and future directions, highlighting the field's unique challenges and recent advances.

Contribution

It provides a systematic classification and analysis of POI recommendation systems, summarizing key contributions, methodologies, and challenges in the field.

Findings

Classification of POI recommendation systems into three taxonomies.

Identification of influential factors such as geographical, social, temporal, and content.

Discussion of datasets and evaluation metrics used in the field.

Abstract

Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey.