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Point-of-interest (POI) recommendation that suggests new places for users tovisit arises with the popularity of location-based social networks (LBSNs). Dueto the importance of POI recommendation in LBSNs, it has attracted muchacademic and industrial interest. In this paper, we offer a systematic reviewof this field, summarizing the contributions of individual efforts andexploring their relations. We discuss the new properties and challenges in POIrecommendation, compared with traditional recommendation problems, e.g., movierecommendation. Then, we present a comprehensive review in three aspects:influential factors for POI recommendation, methodologies employed for POIrecommendation, and different tasks in POI recommendation. Specifically, wepropose three taxonomies to classify POI recommendation systems. First, wecategorize the systems by the influential factors check-in characteristics,including the geographical information, social relationship, temporalinfluence, and content indications. Second, we categorize the systems by themethodology, including systems modeled by fused methods and joint methods.Third, we categorize the systems as general POI recommendation and successivePOI recommendation by subtle differences in the recommendation task whether tobe bias to the recent check-in. For each category, we summarize thecontributions 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 thissurvey.

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