FORCE: A Framework of Rule-Based Conversational Recommender System


  • Jun Quan School of Computer Science and Technology, Soochow University, Suzhou, China Microsoft, China
  • Ze Wei Microsoft, China
  • Qiang Gan Microsoft, China
  • Jingqi Yao Donald Bren School of Information and Computer Sciences, University of California, Irvine Microsoft, China
  • Jingyi Lu Microsoft, China
  • Yuchen Dong Microsoft, China
  • Yiming Liu Microsoft, China
  • Yi Zeng Microsoft, China
  • Chao Zhang Microsoft, China
  • Yongzhi Li Microsoft, China
  • Huang Hu Microsoft, China
  • Yingying He Microsoft, China
  • Yang Yang Microsoft, China
  • Daxin Jiang Microsoft, China



CRS, Conversational Recommender Systems, Conversational Recommendation, Cold-start Conversation, Cold-start Recommendation


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.




How to Cite

Quan, J., Wei, Z., Gan, Q., Yao, J., Lu, J., Dong, Y., Liu, Y., Zeng, Y., Zhang, C., Li, Y., Hu, H., He, Y., Yang, Y., & Jiang, D. (2022). FORCE: A Framework of Rule-Based Conversational Recommender System. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13215-13217.