Toward Knowledge-Enriched Conversational Recommendation Systems
Tong Zhang, Yong Liu, Boyang Li, Peixiang Zhong, Chen Zhang, Hao Henry Wang, Chunyan Miao · 2022
Conversational Recommendation Systems recommend items through language based interactions with users.In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie.We also propose an alignment loss to further integrate KG entities into the response generation network.Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.