Sellybot: Conversational Recommender System Based on Functional Requirements

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.