A Matrix Factorization Algorithm for Efficient Recommendations in Social Rating Networks Using Constrained Optimization

In recent years the emergence of social media has become more prominent than ever. Social networking has become the de facto tool used by people all around the world for information discovery. Consequently, the importance of recommendations in a social network setting has urgently emerged, but unfortunately, many methods that have been proposed in order to provide recommendations in social networks cannot produce scalable solutions, and in many cases are complex and difficult to replicate unless the source code of their implementation has been made publicly available. However, as the user base of social networks continues to grow, the demand for developing more efficient social network-based recommendation approaches will continue to grow as well. In this paper, following proven optimization techniques from the domain of machine learning with constrained optimization, and modifying them accordingly in order to take into account the social network information, we propose a matrix factorization algorithm that improves on previously proposed related approaches in terms of convergence speed, recommendation accuracy and performance on cold start users. The proposed algorithm can be implemented easily, and thus used more frequently in social recommendation setups. Our claims are validated by experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset crawled from Flixster.com.