FORCE: A Framework of Rule-Based Conversational Recommender System
This work addresses the cold-start problem in industrial conversational recommender systems, offering a practical solution for developers, though it is incremental as it builds on existing rule-based approaches.
The authors tackled the problem of conversational recommender systems being limited by large-scale annotated data and cold-start scenarios by proposing FORCE, a rule-based framework that allows developers to quickly build bots through configuration, and they verified its effectiveness and usability on two datasets in different languages and domains.
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.