Ontology-based Conversational Recommender System for Recommending Camera
Dublin Core
Title
Ontology-based Conversational Recommender System for Recommending Camera
Subject
camer; conversational recommender system; knowledge-based recommender system; ontology
Description
The camera is a product that has developed very quickly in terms of specifications and functions. In addition, the cameras
available on the market are becoming increasingly varied, so customers need more time to find a camera that suits their needs.
Currently, many recommender systems have been developed to assist users in finding suitable products, especially the
conversational recommender system (CRS). CRS is a recommender system that recommends products through conversations
between the user and the system. However, many developed CRS still forces users to have knowledge of the product's technical
characteristics. In the real world, many people are not familiar with the technical features of products, especially cameras.
People interact more easily with CRS by stating the camera function they want. In this study, we call that statement functional
requirements. Therefore, we proposed a CRS for recommending cameras that interact with users using functional
requirements. This CRS uses semantic reasoning techniques on ontologies. To evaluate system performance, we use two
parameters, i.e., user satisfaction and recommendation accuracy. The evaluation results show that the accuracy of the
recommendations is at a value of 82.35%, and the level of user satisfaction reaches 0.66. With these results, the system can
provide recommendations accurately and satisfy users
available on the market are becoming increasingly varied, so customers need more time to find a camera that suits their needs.
Currently, many recommender systems have been developed to assist users in finding suitable products, especially the
conversational recommender system (CRS). CRS is a recommender system that recommends products through conversations
between the user and the system. However, many developed CRS still forces users to have knowledge of the product's technical
characteristics. In the real world, many people are not familiar with the technical features of products, especially cameras.
People interact more easily with CRS by stating the camera function they want. In this study, we call that statement functional
requirements. Therefore, we proposed a CRS for recommending cameras that interact with users using functional
requirements. This CRS uses semantic reasoning techniques on ontologies. To evaluate system performance, we use two
parameters, i.e., user satisfaction and recommendation accuracy. The evaluation results show that the accuracy of the
recommendations is at a value of 82.35%, and the level of user satisfaction reaches 0.66. With these results, the system can
provide recommendations accurately and satisfy users
Creator
Restu Aditya Rachman, Z.K.A. Baizal
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
June 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Restu Aditya Rachman, Z.K.A. Baizal, “Ontology-based Conversational Recommender System for Recommending Camera,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/9970.