  Sequential recommender systems stand out for their ability to capture users'
dynamic interests and the patterns of item-to-item transitions. However, the
inherent openness of sequential recommender systems renders them vulnerable to
poisoning attacks, where fraudulent users are injected into the training data
to manipulate learned patterns. Traditional defense strategies predominantly
depend on predefined assumptions or rules extracted from specific known
attacks, limiting their generalizability to unknown attack types. To solve the
above problems, considering the rich open-world knowledge encapsulated in Large
Language Models (LLMs), our research initially focuses on the capabilities of
LLMs in the detection of unknown fraudulent activities within recommender
systems, a strategy we denote as LLM4Dec. Empirical evaluations demonstrate the
substantial capability of LLMs in identifying unknown fraudsters, leveraging
their expansive, open-world knowledge.
  Building upon this, we propose the integration of LLMs into defense
strategies to extend their effectiveness beyond the confines of known attacks.
We propose LoRec, an advanced framework that employs LLM-Enhanced Calibration
to strengthen the robustness of sequential recommender systems against
poisoning attacks. LoRec integrates an LLM-enhanced CalibraTor (LCT) that
refines the training process of sequential recommender systems with knowledge
derived from LLMs, applying a user-wise reweighting to diminish the impact of
fraudsters injected by attacks. By incorporating LLMs' open-world knowledge,
the LCT effectively converts the limited, specific priors or rules into a more
general pattern of fraudsters, offering improved defenses against poisoning
attacks. Our comprehensive experiments validate that LoRec, as a general
framework, significantly strengthens the robustness of sequential recommender
systems.
