Improving Conversational Recommendation Systems via Counterfactual Data Simulation
Xiaolei Wang, Kun Zhou, Xinyu Tang, Wayne Xin Zhao, Fan Pan, Zhao Cao, Ji-Rong Wen
Abstract
Conversational recommender systems~(CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data.
Topics & Concepts
Computer scienceCounterfactual thinkingRecommender systemScarcityTraining setTraining (meteorology)Artificial intelligenceMachine learningNatural language processingPhysicsMeteorologyPhilosophyEpistemologyEconomicsMicroeconomicsTopic ModelingRecommender Systems and TechniquesSpeech and dialogue systems