Online Learning and Detecting Corrupted Users for Conversational Recommendation Systems

Conversational recommendation systems (CRSs) are increasingly prevalent, but they are susceptible to the influence of corrupted user behaviors, such as deceptive click ratings. These behaviors can skew the recommendation process, resulting in suboptimal results. Traditional bandit algorithms, which are typically oriented to single users, do not capitalize on implicit social connections between users, which could otherwise enhance learning efficiency. Furthermore, they cannot identify corrupted users in a real-time, multi-user environment. In this paper, we propose a novel bandit problem, Online Learning and Detecting Corrupted Users (OLDCU), to learn and utilize unknown user relations from disrupted behaviors to speed up learning and detect corrupted users in an online setting. This problem is non-trivial due to the dynamic nature of user behaviors and the difficulty of online detection. To robustly learn and leverage the unknown relations among potentially corrupted users, we propose a novel bandit algorithm RCLUB-WCU, incorporating a conversational mechanism. This algorithm is designed to handle the complexities of disrupted behaviors and to make accurate user relation inferences. To detect corrupted users with bandit feedback, we further devise a novel online detection algorithm, OCCUD, which is based on RCLUB-WCU’s inferred user relations and designed to adapt over time. We prove a sub-linear regret bound for RCLUB-WCU, demonstrating its efficiency. We also analyze the detection accuracy of OCCUD, showing its effectiveness in identifying corrupted users. Through extensive experiments, we validate the performance of our methods. Our results show that RCLUB-WCU and OCCUD outperform previous bandit algorithms and achieve high corrupted user detection accuracy, providing robust and efficient solutions in the field of CRSs.