An Improved Restaurant Recommendation Algorithm Based on User's Multiple Features

It is challenging to find the appropriate restaurant using diangping.com. In this paper, regarding the characteristics of restaurant recommender systems, the restaurant recommendation based on the improved collaborative filtering method (ICFM) is proposed by analyzing the users' features. The ICFM considers the influence of the user him- or herself, the similarity of user preferences and the follow relationship. Finally, an experiment is executed on the data set crawled from dianping.com: dianP. The experimental results show that the proposed method obtains better performance than the existing methods.