Conversational recommender system is a tool to help customer in deciding products they are going to buy, by conversational mechanism. By this mechanism, the system is able to imitate natural conversation between customer and professional sales support, for eliciting customer preference. However, many customers are not familiar with the technical features of multi-function and multi-feature products. A more natural way to explore customer preferences is by asking what they want to use with the product they are looking for (product functional requirements). Therefore, this paper proposes a computational model incorporating product functional requirements for interaction. The proposed model covers ontology and its structure as well as algorithms for generating interaction that comprises asking question, recommending products and presenting explanation of why a product is recommended. Based on our user studies, both expert users (familiar with product technical features) and novice users (not familiar with product technical feature) prefer our proposed interaction model than that of the flat interaction model (interaction model based on technical features). Meanwhile, functional requirements-based explanation is able to improve user trust in recommended products by 30% for novice users and 17% for expert users.

会话推荐系统是一种工具，可通过会话机制帮助客户确定要购买的产品。通过这种机制，该系统能够模仿客户和专业销售支持之间的自然对话，以引起客户的偏爱。但是，许多客户并不熟悉多功能和多功能产品的技术功能。探索客户偏好的一种更自然的方法是，询问他们想要与他们要寻找的产品一起使用什么（产品功能要求）。因此，本文提出了一种包含产品功能交互需求的计算模型。提出的模型涵盖了本体及其结构，以及用于生成包括询问，推荐产品并提供推荐原因的解释。根据我们的用户研究，专家用户（熟悉产品技术功能）和新手用户（不熟悉产品技术功能）都喜欢我们建议的交互模型，而不是扁平交互模型（基于技术特征的交互模型）。同时，基于功能需求的说明能够使新产品用户对推荐产品的用户信任度提高30％，专家用户对推荐产品的信任度提高17％。