Paper ID: 2203.10001

FORCE: A Framework of Rule-Based Conversational Recommender System

Jun Quan, Ze Wei, Qiang Gan, Jingqi Yao, Jingyi Lu, Yuchen Dong, Yiming Liu, Yi Zeng, Chao Zhang, Yongzhi Li, Huang Hu, Yingying He, Yang Yang, Daxin Jiang

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 the requirement of large-scale human-annotated datasets. Such methods are not able to deal with the cold-start scenarios in industrial products. To alleviate the problem, we propose FORCE, a Framework Of Rule-based Conversational Recommender system that helps developers to quickly build CRS bots by simple configuration. We conduct experiments on two datasets in different languages and domains to verify its effectiveness and usability.

Submitted: Mar 18, 2022