Alleviating the Long-Tail Problem in Conversational Recommender Systems
Zhipeng Zhao, Kun Zhou, Xiaolei Wang, Wayne Xin Zhao, Fan Pan, Zhao Cao, Ji-Rong Wen
Abstract
Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, i.e., a large proportion of items are rarely (or even never) mentioned in the conversations, which are called long-tail items. As a result, the CRSs trained on these datasets tend to recommend frequent items, and the diversity of the recommended items would be largely reduced, making users easier to get bored.
Topics & Concepts
Recommender systemComputer scienceQuality (philosophy)Diversity (politics)Service (business)Artificial intelligenceInformation retrievalWorld Wide WebNatural language processingEconomyEconomicsAnthropologyEpistemologyPhilosophySociologyRecommender Systems and TechniquesTopic ModelingAdvanced Graph Neural Networks