Toward Robust Recommendation via Real-time Vicinal Defense
Yichang Xu Chenwang Wu Defu Lian
Recommender systems have been shown to be vulnerable to poisoning attacks,
where malicious data is injected into the dataset to cause the recommender
system to provide biased recommendations. To defend against such attacks,
various robust learning methods have been proposed. However, most methods are
model-specific or attack-specific, making them lack generality, while other
methods, such as adversarial training, are oriented towards evasion attacks and
thus have a weak defense strength in poisoning attacks.
  In this paper, we propose a general method, Real-time Vicinal Defense (RVD),
which leverages neighboring training data to fine-tune the model before making
a recommendation for each user. RVD works in the inference phase to ensure the
robustness of the specific sample in real-time, so there is no need to change
the model structure and training process, making it more practical. Extensive
experimental results demonstrate that RVD effectively mitigates targeted
poisoning attacks across various models without sacrificing accuracy. Moreover,
the defensive effect can be further amplified when our method is combined with
other strategies.