@inproceedings{quan2022force,
author = {Quan, Jun and Wei, Ze and Gan, Qiang and Yao, Jingyi and Lu, Jingyi and Dong, Yuchen and Liu, Yiming and Zeng, Yi and Zhang, Chao and (李永智), Yongzhi Li and Hu, Huang and He, Yingying and Yang, Yang and Jiang (姜大昕), Daxin},
title = {FORCE: A Framework of Rule-Based Conversational Recommender System},
booktitle = {AAAI 2022},
year = {2022},
month = {February},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/force-a-framework-of-rule-based-conversational-recommender-system/},
}