Recommender systems are designed to suggest items based on user preferences, helping users navigate the vast amount of information available on the internet. Given the overwhelming content, outlier detection has emerged as a key research area in recommender systems. It involves identifying unusual or suspicious patterns in user behavior. However, existing studies in this field face several challenges, including the limited universality of algorithms, difficulties in selecting users, and a lack of optimization. In this paper, we propose an approach that addresses these challenges by employing various clustering algorithms. Specifically, we utilize a user-user matrix-based clustering technique to detect outliers. By constructing a user-user matrix, we can identify suspicious users in the system. Both local and global outliers are detected to ensure comprehensive analysis. Our experimental results demonstrate that this approach significantly improves the accuracy of outlier detection in recommender systems.

翻译：推荐系统旨在根据用户偏好推荐项目，帮助用户在互联网海量信息中进行导航。鉴于内容规模庞大，异常检测已成为推荐系统中的一个关键研究领域，其核心在于识别用户行为中的异常或可疑模式。然而，该领域的现有研究面临若干挑战，包括算法普适性有限、用户选择困难以及缺乏优化等。本文提出一种通过采用多种聚类算法来应对这些挑战的方法。具体而言，我们利用基于用户-用户矩阵的聚类技术来检测异常值。通过构建用户-用户矩阵，我们能够识别系统中的可疑用户。该方法同时检测局部与全局异常值，以确保分析的全面性。实验结果表明，该方法显著提升了推荐系统中异常检测的准确性。