FORCE: A Framework of Rule-Based Conversational Recommender System

The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which are largely limited by requirement large-scale human-annotated datasets. Such methods not able to deal with cold-start scenarios industrial products. To alleviate problem, we propose FORCE, a Framework Of Rule-based Conversational rEcommender system that helps developers quickly build CRS bots simple configuration. We conduct experiments two datasets different languages and domains verify its effectiveness usability.