The popular and ubiquitous location-based social networks (LBSNs) ap-peal many users for sharing interesting locations with other users. As the collected data (such as users"e profiles, location comment, and suggestion) become a lot larger in size, location-based recommender systems require more effective fillters to be able to suggest potentially preferable locations to users. Location recommendation is more difficult and challenging especially if users have few or no check-in histories as a new user. Therefore, instead of depending on users"e check-in histories, previous works focused on creating recommended location lists by leveraging the information given by other users who check in locations in each area. However, previous studies took into account only the frequencies of users for creating recommendation lists and have su˙ered from the cold-start problem where new users have few or none histories. As a result, the recommenders hardly suggest any locations matching the users"e preference. In this paper, we propose an enhanced location-recommendation approach called N-most interesting location-based recommender system (NILR) to recommend interesting lo-cations for new users. Our approach can be divided into three phases. First, the NILR discovers interesting locations by taking into account both the visiting frequencies and the preferences of users already in the system.ETC...
Keywords
Location-based recommender systems, Non- pro le users, Ranking
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