FORCE: A Framework of Rule-Based Conversational Recommender System
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 12 references that are in the map. First posted on arXiv 18 Mar 2022.
Builds on
10 of 12 references.
References · 10
- 2018Towards Deep Conversational Recommendationscited by 174
- 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
- 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
- 2019Towards Knowledge-Based Recommender Dialog Systemcited by 112
- 2020Towards Conversational Recommendation over Multi-Type Dialogscited by 107
- 2020Towards Topic-Guided Conversational Recommender Systemcited by 94
- 2020INSPIRED: Toward Sociable Recommendation Dialog Systemscited by 91
- 2019End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosiscited by 70
- 2019Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialoguecited by 44
- 2021CRSLab: An Open-Source Toolkit for Building Conversational Recommender Systemcited by 34
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Citing papers per year: 2024: 1.
1 paper.