Learning to make social recommendations: a model-based approach

Social recommendation, predicting people who match other people for friendship or as potential partners in life or work, has recently become an important task in many social networking sites. Traditional content-based and collaborative filtering methods are not sufficient for people-to-people recommendation because a good match depends on the preferences of both sides. We proposed a framework for social recommendation and develop a representation for classification of interactions in online dating applications that combines content from user profiles plus interaction behaviours. We show that a standard algorithm can be used to learn a model to predict successful interactions. We also use a method to search for the best model by minimising a cost based on predicted precision and recall. To use the model in real world applications to make recommendations, we generate candidate pairs using the selected models and ranked them using a novel probabilistic ranking function to score the chance of success. Our model-based social recommender system is evaluated on historical data from a large commercial social networking site and shows improvements in success rates over both interactions with no recommendations and those with recommendations generated by standard collaborative filtering.