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 a 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, and 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, spatiotemporal information-based user modeling, and geosocial information-based user modeling. Finally, summarizing the existing works, we point out the future challenges and new directions in five possible aspects.
Highlights
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
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 a check-in behavior can make real-life daily experiences spread quickly over the Internet. 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 location-based services. us, location-based social media data services are attracting significant attention from different commerce domains, for example, user profiling [1,2,3], recommendation systems [4, 5], urban emergency event management [6,7,8,9], urban planning [10], and marketing decisions [11]
(2) Considering the characteristics of geographical and social data in LBSNs, we present a formalization of user modeling for POI recommendations in LBSNs
Summary
Geographical locations and temporal information are the main components of users’ check-ins that are recorded in LBSNs, social activities and social media shared via LBSNs are typically labeled with a location tag. A tourist may share photos with his friends via WeChat (it has become an important social media platform in China; it provides users an innovative way to communicate and interact with friends through text messaging, one-to-many messaging, hold-to-talking voice messaging, photo/video sharing, location sharing, and contact information exchange (https://en.wikipedia.org/wiki/WeChat)) When he visits Olympic park in Beijing, first, his current geographical location and the time will be recorded by WeChat through his check-ins. Foursquare (a social-driven location sharing and local search-and-discovery service mobile app (https://en.wikipedia.org/wiki/Foursquare)) had approximately 55 million monthly active users and 10 billion checkins by December 2016 (http://expandedramblings.com/ index.php/by-the-numbers-interesting-foursquare-user-stats/). The average number of daily check-ins on Foursquare is 8 million (https://en.wikipedia.org/wiki/ Yelp), and we can determine that the average number of daily check-ins for each user on Foursquare is (8 × 30)/55 ≈ 4.36
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