A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS.

The popularity of location-based social networks (LBSN) provides us with a new perspective for understanding people's travel behaviours and enables a lot of location-based services, such as point of interest (POI) recommendation. However, personalized POI recommendation is very challenging, as the user-location matrix is very sparse for traditional collaborative filtering (CF)-based POI recommendation approaches. The problem becomes even more challenging when people travel to a new city. In addition, temporal influence plays an important role in POI recommendation, for most users tend to visit different kinds of POIs at different time in a day, e.g., visiting a food-related POI at noon and visiting a nightlife spot at night. To the end, we propose a novel POI recommendation system, which consists of two components: context-aware tensor decomposition (CTD) for user preferences modelling and weighted HITS (Hypertext Induced Topic Search)-based POI rating (WHBPR). We model user preferences with a three-dimension tensor (user-category-time). Supplementing the missing entries of the tensor through CTD with the aid of other three matrices, we recover user preferences of different time slots. WHBPR incorporates the impacts of user preferences and social opinions on POI rating. We evaluated our method using the real Foursquare datasets, verifying the advantages of our method beyond other baselines.