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.
@article{arxiv.2203.10001,
title = {FORCE: A Framework of Rule-Based Conversational Recommender System},
author = {Jun Quan and Ze Wei and Qiang Gan and Jingqi Yao and Jingyi Lu and Yuchen Dong and Yiming Liu and Yi Zeng and Chao Zhang and Yongzhi Li and Huang Hu and Yingying He and Yang Yang and Daxin Jiang},
journal= {arXiv preprint arXiv:2203.10001},
year = {2022}
}