Affiliations: [a] Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing, China | [b] Graduate School of Big Data and Software Engineering, Chongqing University, Chongqing, China
Abstract: Point of Interest (POI) recommendation aims to recommend places which users have not visited before. In this paper, we proposed a POI recommendation method using user similarity, which assumes that people may be interested in the places that others have been to but they have not visited before. In this paper, one day can be divided into 24 time-slots, thus each hour can be defined as a time slot. The novelty of the method we proposed lies in user features which adopted by the summation of user’s check-in times in each time slot. The check-in times for each user can be collected and then form a vector, and we can take advantage of the summation of these check-in times in each time slot to find out user characteristics. The similarity between any two users can be calculated by cosine similarity method. Then a sorted list of scores which includes all unvisited locations of each user can be obtained according to user similarity. Through these steps, a POI recommendation list can be produced according to the score from high to low. The experimental result indicates that the method we proposed in this paper is effective.
Keywords: POI recommendation, time slot, user similarity, check-in time
