Recommender systems play an essential role in solving information overload but face openness challenges and are vulnerable to malicious attacks that can lead to data tampering, affecting recommendation results and jeopardizing the security of recommendation services. Existing robust recommendations that detect and block attacks directly from data may result in normal behavior being flagged as an attack being blocked. Statistical-based methods for manually extracting features from rating data are time-consuming and insufficient for sophisticated attacks. Embedded learning-based methods lack characterization of similar information and multi-order interactions. The imbalance between attack size and normal behavior also poses a challenge. To this end, we propose a robust recommendation-oriented malicious attack detection method. The attack detection component of the method utilizes graph learning to model the higher-order connectivity of user-item interaction graphs and Convolutional Neural Network (CNN) assisted feature extraction, and it is combined with an ensemble approach to deal with the imbalance problem to detect attacks. The recommendation component automatically determines the contribution of a user's behavior based on the probability that a click is recognized as normal. The experimental results show that the method performs well in malicious attack detection and is more robust to co-visitation injection attacks in recommendations.
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