Abstract
Recommender systems aim to support users in decision-making through the knowledge extracted from historical ratings. However, many of these ratings may be noisy and/or missing, causing degradation in the quality of the recommendations. Considering these issues, this paper presents a new one-class classifier to predict ratings in recommendation systems. The proposed method estimates the shared informative neighbors of each user by a probability fuzzy rough set method. Since the fuzzy rough set theory is sensitive to noisy samples, the quarter-sphere SVM classifier is designed to reduce the impact of noise on the results. The proposed classifier can satisfactorily determine a boundary around the target class while it reduces the acceptance probability of the outliers and non-target class(es). The theoretical interpretations are provided to prove the statistical stability of the proposed method. Also, noise analysis has been carried out. Through extensive experiments on several real-world data sets, it is confirmed that the proposed method outperforms the other six methods in terms of accuracy, recall, precision, and computational time.
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