Sellybot: Conversational Recommender System Based on Functional Requirements

Nurani Solechah, Z. Baizal, N. Ikhsan
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Abstract

Recently, high-tech products are very fast in issuing new types. For example, smartphones have various brands and types with different specifications. This condition triggers doubts among the public to buy the product due to limited knowledge about the technical specifications that suit their needs. Therefore, it is necessary to develop a recommender system based on product functional requirements. In our prior work, a Conversational Recommender System (CRS) has been developed to recommend smartphones based on high-level requirements (product functional requirements) by combining Navigation by Asking (NBA) and Navigation by Proposing (NBP). Thus, users who are unfamiliar with the technical features of the product can express their needs more easily. However, the system uses a dialog form, so users are still less flexible in expressing their needs. In this study, we further develop this research by building Sellybot, a CRS that uses natural language in its interactions with users. We built Sellybot using the RASA framework. Evaluation is done by observing the accuracy and user satisfaction. The evaluation results show that the system has an accuracy of 84.8% and for the questionnaire, it is found that 80.3% of users choose Sellybot, where users feel more flexible in using the system, and get a better experience.
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Sellybot:基于功能需求的会话推荐系统
最近,高科技产品的新产品层出不穷。例如,智能手机有不同的品牌和类型,不同的规格。这种情况引发了公众对购买该产品的疑虑,因为他们对适合自己需求的技术规格了解有限。因此,有必要开发一个基于产品功能需求的推荐系统。在我们之前的工作中,我们已经开发了一个会话推荐系统(CRS),通过结合询问导航(NBA)和建议导航(NBP),根据高级需求(产品功能需求)推荐智能手机。这样,不熟悉产品技术特性的用户可以更容易地表达他们的需求。然而,该系统使用对话框形式,因此用户在表达他们的需求时仍然不够灵活。在这项研究中,我们通过构建Sellybot来进一步发展这一研究,Sellybot是一种在与用户交互时使用自然语言的CRS。我们使用RASA框架构建Sellybot。评估通过观察准确性和用户满意度来完成。评估结果表明,系统的准确率为84.8%,对于问卷调查,发现80.3%的用户选择Sellybot,用户在使用系统时感觉更灵活,获得了更好的体验。
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