RESEARCH ON PERSONALIZED RECOMMENDATION ALGORITHM FUSING TIME AND LOCATION

With development of recommendation systems, they are faced with more and more challenges. In order to relieve problems existing in commodity selection by users of different preferences from different regions, personalized recommendation based on location information has emerged. Nowadays most recommendation systems based on location information neglect the fact that users’ preference will change with time. To solve the above problem, geographic location and time factor of users are effectively combined in this paper, and a personalized recommendation algorithm TLPR combining time and location information is proposed. This algorithm determines the users’ geographic location according to postcode information of the users, uses pyramid quadtree model to distribute users into nodes at each layer in the pyramid, utilizes collaborative filtering algorithm for local recommendation in each node, introduces a time function to regulate time-dependent change of user interests when calculating user similarity at each node and finally realizes a comprehensive recommendation by distributing a weight for recommendation result at each layer in the pyramid quadtree. A comparative experience is carried out for recommendation performance of this algorithm on MovieLens dataset, and experimental results indicate that this algorithm is of better recommendation effect