论文标题
武力：基于规则的对话推荐系统的框架
FORCE: A Framework of Rule-Based Conversational Recommender System
论文作者
论文摘要
近年来，对话推荐系统（CRS）受到了广泛的关注。但是，大多数现有作品都集中在各种深度学习模型上，这些模型在很大程度上受到大规模人类宣布数据集的要求的限制。这种方法无法处理工业产品中的冷启动方案。为了减轻问题，我们提出了Force，这是一个基于规则的对话推荐系统的框架，可帮助开发人员通过简单的配置快速构建CRS机器人。我们在两个不同语言和域的两个数据集上进行实验，以验证其有效性和可用性。
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.
