Research Area:  Machine Learning

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.

Keywords:  
CRS
Conversational Recommender Systems
Conversational Recommendation
Cold-start Conversation
Cold-start Recommendation

Author(s) Name:   Jun Quan , Ze Wei , Qiang Gan

Publisher name:  PKP Publishing Services Network

DOI:  10.1609/aaai.v36i11.21732

Volume Information:  Volume 36

Paper Link:   https://ojs.aaai.org/index.php/AAAI/article/view/21732