Preference-oriented mining techniques for location-based store search

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