Author(s): Li Bian, Henry Holtzman
Most social network websites rely on people’s proximity on the social graph for friend recommendation. In this paper, we present MatchMaker, a collaborative filtering friend recommendation system based on personality matching. The goal of MatchMaker is to leverage the social information and mutual understanding among people in existing social network connections, and produce friend recommendations based on rich contextual data from people’s physical world interactions. MatchMaker allows users’ network to match them with similar TV characters, and uses relationships in the TV programs as parallel comparison matrix to suggest to the users friends that have been voted to suit their personality the best. The system’s ranking schema allows progressive improvement on the personality matching consensus and more diverse branching of users’ social network connections. Lastly, our user study shows that the application can also induce more TV content consumption by driving users’ curiosity in the ranking process.