Ontology-Based Conversation Recommender System for Camera Using Collaborative Filtering

In this digital age, cameras have become one of the most popular electronic items in the technology market due to their diverse uses. However, potential buyers often need help in choosing a camera that matches the required camera specifications. By using ontologies, this system will provide accurate and personalized camera recommendations. However, the challenges include creating a suitable ontology, updating the ontology regularly, and limited camera data. To address these issues, we propose an ontology-based camera recommender system that not only provides accurate and personalized guidance to users but can also provide added value to camera manufacturers in increasing product sales. Furthermore, this research introduces a knowledge ontology update and implementation of the Conversational Recommender System (CRS) framework in the camera domain, we propose using a collaborative filtering method to improve the recommendation accuracy. The results are expected to provide better guidance to users in selecting cameras according to their functional needs. We measured both system performance and user satisfaction levels in the evaluation process. Based on the evaluation results, the accuracy of the proposed system is 88.17%. This result is better than previous research that only relies on ontology.