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Recommender systems are often susceptible to well-crafted fake profiles,leading to biased recommendations. The wide application of recommender systemsmakes studying the defense against attack necessary. Among existing defensemethods, data-processing-based methods inevitably exclude normal samples, whilemodel-based methods struggle to enjoy both generalization and robustness.Considering the above limitations, we suggest integrating data processing androbust model and propose a general framework, Triple Cooperative Defense (TCD),which cooperates to improve model robustness through the co-training of threemodels. Specifically, in each round of training, we sequentially use thehigh-confidence prediction ratings (consistent ratings) of any two models asauxiliary training data for the remaining model, and the three modelscooperatively improve recommendation robustness. Notably, TCD adds pseudo labeldata instead of deleting abnormal data, which avoids the cleaning of normaldata, and the cooperative training of the three models is also beneficial tomodel generalization. Through extensive experiments with five poisoning attackson three real-world datasets, the results show that the robustness improvementof TCD significantly outperforms baselines. It is worth mentioning that TCD isalso beneficial for model generalizations.

Towards Robust Recommender Systems via Triple Cooperative Defense