Friend Recommendation for Location-Based Mobile Social Networks

Along with the rapid growth of Internet, many social websites are founded, and gradually begin to influence the people´s life. Such as Facebook, the social network site provides the personalized recommendation system with friends-of-friends method to recommend new friends to users. The intuition is derived from the idea that it is more probable a person will know a friend of their friends rather than a random person. However, this approach does not consider any insights into human cognitive components such as social interactions. Thus, we propose a brand-new friend recommendation approach. The main concept is to recommend friends who have the similar interests or another thing with self to users. Besides utilizing the information on social networks, such as interests, the concept of real-life location and dwell time is further added in our approach. In this paper, we develop two comparison methods to provide quality friend recommendation. First method combines the existing landmark and user´s dwell time at certain landmark to make the Voronoi diagram, and analyzes location similarity between users. Second methods is to analyze the interest lists from each social network accounts by using pattern matching and finding longest common subsequence. Through this two comparison methods, we assess the acceptable degree between two, and successfully implement the friend recommendation system.