A review on matrix factorization techniques in recommender systems

Growth of the Internet and web applications has led to vast amount of information over web. Information filtering systems such as Recommenders have become potential tools to deal with such plethora of information, help users select and provide relevant information. Collaborative Filtering is the popular approach to recommendation systems. Collaborative Filtering works on the fact that users with similar behavior will have similar interests in future, and using this notion collaborative filtering recommends items to user. However, the sparseness in data and high dimensionality has become a challenge. To resolve such issues, model based, matrix factorization techniques have well emerged. These techniques have evolved from using simple user-item rating information to auxiliary information such as time and trust. In this paper, we present a comprehensive review on such matrix factorization techniques and their usage in recommenders.