Preference-oriented mining techniques for location-based store search

With the development of wireless telecommunication technologies, a number of studies have been done on the issues of location-based services due to wide applications. Among them, one of the active topics is the location-based search. Most of previous studies focused on the search of nearby stores, such as restaurants, hotels, or shopping malls, based on the user's location. However, such search results may not satisfy the users well for their preferences. In this paper, we propose a novel data mining-based approach, named preference-oriented location-based search (POLS), to efficiently search for k nearby stores that are most preferred by the user based on the user's location, preference, and query time. In POLS, we propose two preference learning algorithms to automatically learn user's preference. In addition, we propose a ranking algorithm to rank the nearby stores based on user's location, preference, and query time. To the best of our knowledge, this is the first work on taking temporal location-based search with automatic user preference learning into account simultaneously. Through experimental evaluations on the real dataset, the proposed approach is shown to deliver excellent performance.