FORCE: A Framework of Rule-Based Conversational Recommender System
Keywords:CRS, Conversational Recommender Systems, Conversational Recommendation, Cold-start Conversation, Cold-start Recommendation
AbstractThe 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. https://doi.org/10.1609/aaai.v36i11.21732
AAAI Demonstration Track