Zero-Sum Reward and Punishment Collaborative Filtering Recommendation Algorithm

In this paper, we propose a novel memory-based collaborative filtering recommendation algorithm. Our algorithm use a new metric named influence weight, which is adjusted with zero-sum reward and punishment mechanism whenever the active user provides a new rating, to select neighbors and weight their opinions. Since the weight of personalized ratings, which contain more value for searching similar neighbors, is magnified appropriately in the formation of influence weight, our algorithm can find similar neighbors more effectively and filter the fake users introduced by shilling attacks automatically. When predicting for the active user, our algorithm select neighbors with the Top-N largest positive influence weights and predict their missing ratings. This rating smoothing method can alleviate data sparsity more efficiently. Then it computes the weighted average of all the selected neighbors´ opinions and generates recommendations. Empirical results confirm that our algorithm achieves significant progress in all aspects of accuracy, scalability, robustness against data sparsity and shilling attacks simultaneously.