AdaMF: Adaptive Boosting Matrix Factorization for Recommender System

Matrix Factorization (MF) is one of the most popular approaches for recommender systems. Existing MF-based recommendation approaches mainly focus on the prediction of the users’ ratings on unknown items. The performance is usually evaluated by the metric Root Mean Square Error (RMSE). However, achieving good performances in terms of RMSE does not guarantee a good performance in the top-N recommendation. Therefore, we advocate that treating the recommendation as a ranking problem. In this study, we present a ranking-oriented recommender algorithm AdaMF, which combines the MF model with AdaRank. Specifically, we propose an algorithm by adaptively combining component MF recommenders with boosting methods. The combination shows superiority in both ranking accuracy and model generalization. Normalized Discounted Cumulative Gain (NDCG) is chosen as the parameter of the coefficient function for each MF recommenders. In addition, we compare the proposed approach with the traditional MF approach and the state-of-the-art recommendation algorithms. The experimental results confirm that our proposed approach outperforms the state-of-the-art approaches.