Abstract: Present social networking services suggest friends to users based on their social activities, which may not be the most suitable to react a users taste on friend choice in real life. In this project, we present Friendswall, a semantic-based friend recommendation system for social networks, which present friends to users based on their life styles instead of social activities. By taking benefit of sensor-rich Smartphones, Friendswall discovers life styles of users, measures the resemblance of life styles between users, and suggest friends to users if their life styles have high resemblance. Inspired by text mining, in this project we design a users daily life as life documents, from which his/her life styles are taken out by using the Naive Byes algorithm. We further aim a similarity metric to measure the resemblance of life styles between users, and estimate users impact in terms of life styles with a friend-matching graph. Upon acquiring a request, FriendsWall returns a list of people with maximum resemblance scores to the query user. At last, FriendsWall incorporate a feedback mechanism to further amend the recommendation accuracy. This project will build FriendsWall and evaluate its performance on both small-scale experiments and large-scale model.
Keywords: Friend recommendation, Mobile sensing, Social networks, Life style, Data Mining, Machine Learning.