User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art

User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art Shudong Liu School of Information & Security Engineering, Zhongnan University of Economics & Law, Wuhan 430073, China Correspondence should be addressed to Shudong Liu; bupt.mymeng@gmail.com Abstract: The rapid growth of location-based services (LBSs) has greatly enriched people’s urban lives and attracted millions of users in recent years. Location-based social networks (LBSNs) allow users to check-in at a physical location and share daily tips on points-of-interest (POIs) with their friends anytime and anywhere. Such check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of humans’ daily movement and mobility. This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. First, we briefly introduce the structure and data characteristics of LBSNs, then we present a formalization of user modeling for POI recommendations in LBSNs. Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling, geographical information-based user modeling, spatio-temporal information-based user modeling, and geo-social information-based user modeling. Finally, summarizing the existing works, we point out the future challenges and new directions in five possible aspects. Keywords: social networks, location-based services, point of interest, recommendation system, user profile, collaborative filtering, matrix factorization 1、Introduction The advanced information technologies that have resulted from the rapid growth of location-based services (LBSs) have greatly enriched people’s urban lives. Location-based social networks (LBSNs) allow users to check-in and share their locations, tips, and experiences about points-of-interest (POIs) with their friends anytime and anywhere. For example, while having lunch at a restaurant, we may take photos of the dishes on the table and immediately share these photos with our friends via LBSNs. Such check-in behavior can make real-life daily experiences spread quickly over the Internet. Moreover, such check-in data of LBSNs can be fully exploited to understand the basic laws of human daily movement and mobility [1], which can be applied to recommendation systems and locat -ion-based services. Thus, location-based social media data services are attracting signifi cant attention from different commerce doma -ins, e.g., user profiling [1-3], recommendati -on systems [4,5], urban emergency event man -agement [6-9], urban planning [10] and mark -eting decisions [11]. User generated spatial-temporal data can be collected from LBSNs and can be widely used for understanding and modeling human mobility according to the following four aspects: (1) Geographical feature The spatial features of human movement as hidden in millions of check-in data has been exploited to understand human mobility. For example, people tend to move to nearby places and occasionally to distant places [2, 4], the former is short-ranged travel and is not affected by social network ties, which are periodic both spatially and temporally; the latter is long-distance travel and more influenced by social network ties [1]. (2) Temporal features. the routines and habits of our daily lives, there are different probabilities for different locations at different hours of the day and different days of the week. The check-in data of LBSBs also reveals these results [3,5]. Most people go to work on the weekdays, their check-in behaviors often happen at noon or at night, and the locations they choose are close their workplaces or homes. On the weekends, most check-in behaviors happen in the morning or afternoon, and the locations are close to certain POIs (e.g. a marketplace, restaurant, museum, or scenic spot). (3) Social features First, many research studies [1, 12] show that people tend to visit close places more often than distant places, but they tend to visit distant places close to their friends’ homes or those that are checked-in by their friends. These observations have been widely used for location recommendations in LBSNs [13-15]. Second, the spatial-temporal feature abstract -ted from check-in data has been exploited to infer social ties [16] and friend recommenda -tions [17-19]. (4) Integrated feature As one type of global public data source about individual activity-related choices, the check-in data in LBSNs provides a new way to sense people’s spatial and temporal preferences and infer their social ties. More -over, it always provides a new perspective from which urban structures and related socio

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