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Review-Based Recommender Systems (RBRS) have attracted increasing researchinterest due to their ability to alleviate well-known cold-start problems. RBRSutilizes reviews to construct the user and items representations. However, inthis paper, we argue that such a reliance on reviews may instead expose systemsto the risk of being shilled. To explore this possibility, in this paper, wepropose the first generation-based model for shilling attacks against RBRSs.Specifically, we learn a fake review generator through reinforcement learning,which maliciously promotes items by forcing prediction shifts after addinggenerated reviews to the system. By introducing the auxiliary rewards toincrease text fluency and diversity with the aid of pre-trained language modelsand aspect predictors, the generated reviews can be effective for shilling withhigh fidelity. Experimental results demonstrate that the proposed framework cansuccessfully attack three different kinds of RBRSs on the Amazon corpus withthree domains and Yelp corpus. Furthermore, human studies also show that thegenerated reviews are fluent and informative. Finally, equipped with AttackReview Generators (ARGs), RBRSs with adversarial training are much more robustto malicious reviews.

Shilling Black-box Review-based Recommender Systems through Fake Review Generation