A Restaurant Recommender System Based on User Preference and Location in Mobile Environment

Abstract

Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. In this paper, we propose a restaurant recommender system in mobile environment. This recommender system adopts a user preference model by using the features of user's visited restaurants, and also utilizes the location information of user and restaurants to dynamically generate the recommendation results. Baidu map cloud service is used to implement the proposed recommender system. The result of a case study shows that the proposed restaurant recommender system can effectively utilize user's preference and the location information to recommend the personalized and suitable restaurants for different users.