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.

推荐系统旨在通过从历史评级中提取的知识来支持用户进行决策。然而，这些评级中的许多可能是嘈杂的和/或缺失的，从而导致建议的质量下降。考虑到这些问题，本文提出了一种新的一类分类器来预测推荐系统中的评分。所提出的方法通过概率模糊粗糙集方法估计每个用户的共享信息邻居。由于模糊粗糙集理论对噪声样本敏感，因此设计四分之一球SVM分类器以减少噪声对结果的影响。所提出的分类器可以令人满意地确定目标类周围的边界，同时降低异常值和非目标类的接受概率。提供了理论解释以证明所提出方法的统计稳定性。此外，还进行了噪声分析。通过对几个真实世界数据集的大量实验，证实所提出的方法在准确性、召回率、精确度和计算时间方面优于其他六种方法。