摘要： 协同过滤推荐系统因其开放的架构特性，面临着恶意用户通过攻击模型进行托攻击从而干扰推荐准确性的问题。现有的检测方法根据不同攻击模型的特点从评分中提取特征，由于评分本身的范围限制，导致提取的特征差异性不足，且现有的方法常忽略用户之间的交互信息，使得提取到的检测特征存在完整性缺陷。针对现有问题，提出一种基于加权评分与加权交互的托攻击检测算法。算法使用项目的流行度为评分加权，并将其转化为灰度图像表示，利用卷积神经网络提取更具差异性的评分特征；使用用户的相似度为交互数据加权，利用全连接神经网络有效捕获用户之间的关联特征。最后通过神经网络进行特征融合，得到包含信息更加完整的用户特征，提高分类器在托攻击检测中的性能。在MovieLens-100k数据集上的实验结果表明，在不同填充率和不同攻击强度下，对于不同攻击模型算法均能够有效、稳定地检测。在MovieLens-1m数据集上的实验结果表明算法能够应对四种攻击模型同时存在的较为复杂的情况，具有较好的鲁棒性。

Abstract: Collaborative filtering recommendation systems, due to their open architecture, face the issue of malicious users manipulating the model through shilling attacks to disrupt recommendation accuracy. Existing detection methods extract features from ratings based on the characteristics of different attack models. However, due to the limited range of the ratings themselves, the extracted features lack sufficient distinctiveness. Additionally, existing methods often overlook interaction information among users, resulting in incomplete detection features. To address these limitations, we propose a novel shilling attack detection algorithm based on weighted rating and weighted interaction. The algorithm employs item popularity to weight ratings and transforms them into grayscale image representations, enabling convolutional neural networks to extract more distinctive rating features. Simultaneously, it utilizes user similarity to weight interaction data, with fully connected neural networks effectively capturing inter-user relational features. Finally, feature fusion is performed through a neural network to obtain more comprehensive user features, thereby enhancing the performance of the classifier in shilling attack detection. Experimental results on the MovieLens-100k dataset demonstrate that the algorithm can effectively and stably detect various attack models under different filler rates and attack intensities. Results on the MovieLens-1M dataset show that the algorithm can handle more complex scenarios where four attack models coexist, exhibiting strong robustness.