User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback

Recent conversational recommender systems (CRSs) have achieved considerable success on addressing the cold-start problem. While they utilize conversational key-terms to efficiently elicit user preferences, most of them, however, neglect that key-terms can also introduce biases. Systems learning key-term-level user preferences may make a biased item recommendation based on an overrated key-term instead of the item itself. As key-term conversation is a crucial part of CRSs, it is important to properly handle such bias resulting from the item-key-term relationship. While many debiasing methods have been proposed for traditional recommender systems, most of them focus on items or item groups re-ranking or re-weighting strategies such as calibration and propensity score, which are not designed to model the relation between item and key-term user preference. There is also no effective way for traditional debiasing methods to measure potentially useful biases through conversational key-terms to enhance the recommendation performance.