摘要： 随着互联网平台的快速发展，推荐系统在提供个性化服务的同时，依旧面临着托攻击行为导致的推荐准确度下降问题。现有的托攻击检测算法大多以一个或几个评价指标从用户评分差异的视角进行，且较少考虑用户选择项目的偏好相关性，导致对用户行为特征建模不够全面，易造成检测误判率较高或适用攻击模式较少的问题。针对这一问题，提出一种多视角特征融合的托攻击检测算法。算法根据变分自编码器获取用户评分潜在特征与分布特征的基础上，从评分长短期依赖、概率密度分布的视角学习用户概貌的时空分布特征，结合用户历史偏好相关性，利用神经网络模型进行多视角特征融合，形成具有综合检测能力的用户概貌表示，以提升托攻击检测精度。实验结果表明，本算法对虚假用户检测精度有较大提升，在大多数情况下均能达到95%以上。在不同填充率和不同攻击规模下，该算法对托攻击行为均具有较好的检测效果，且具有较好的鲁棒性。

Abstract: With the rapid development of internet platforms, recommendation systems face the challenge of reduced recommendation accuracy due to shilling attacks, while still providing personalized services. Existing shilling attack detection algorithms mostly focus on a single or a few evaluation metrics from the perspective of user rating differences, and seldom consider the preference correlation of the items selected by users. This leads to insufficient modeling of user behavior characteristics, resulting in high misdetection rates or limited applicability to different attack patterns. To address this issue, a multi-perspective feature fusion shilling attack detection algorithm is proposed. Based on the latent features and distribution characteristics of user ratings obtained through a variational autoencoder, the algorithm learns the spatiotemporal distribution characteristics of user profiles from the perspectives of short- and long-term dependencies of ratings and probability density distributions. By combining the historical preference correlation of users, the algorithm uses a neural network model for multi-perspective feature fusion to form a comprehensive user profile representation with enhanced detection capability, thereby improving shilling attack detection accuracy. Experimental results show that the proposed algorithm significantly improves the detection accuracy of fake users, achieving over 95% accuracy in most cases. The algorithm also demonstrates good detection performance under different filling rates and attack scales, as well as strong robustness.