Laptop Recommender System Using the Hybrid of Ontology-Based and Collaborative Filtering
DOI:
10.33395/sinkron.v9i2.13370Keywords:
collaborative filtering, conversational recommender system, laptop recommender system, ontology-based recommender system, recommender systemAbstract
In the era of ever-evolving information technology, choosing the best laptop can be a complicated task for many users. The increasing complexity of technical specifications is often an obstacle, especially for users who need help understanding them. In addressing this challenge, we propose a solution: a laptop recommendation system that considers users' preferences and functional needs. We designed this system to help users choose a laptop that suits their daily functional needs. This system uses a form of Conversational Recommender System (CRS) by combining Ontology-Based Recommender System Filtering and Collaborative Filtering (CF). Ontology-Based Recommender System Filtering ensures a strong relationship between functional needs and technical specifications of laptops, making it easier for users to identify the right laptop. At the same time, Collaborative Filtering (CF) can provide diversity to the recommended products by using similar user preference data. We evaluate the accuracy of our system by calculating the success rate of recommendation accuracy with the accuracy metric, and the evaluation results show that the success rate of recommendation accuracy reaches 93.33%. Our system is highly effective in assisting users in choosing a laptop that suits their functional needs. With our laptop recommendation system, users can confidently select the correct laptop without being burdened by technical specifications, thus making their lives easier and more productive.
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Abdurahman Baizal, Z. K., Murti, Y. R., & Adiwijaya. (2017). Evaluating functional requirements-based compound critiquing on conversational recommender system. 2017 5th International Conference on Information and Communication Technology, ICoIC7 2017, 0(c). https://doi.org/10.1109/ICoICT.2017.8074656
Ahmed, B., Wang, L., Hussain, W., Qadoos, M. A., Tingyi, Z., Amjad, M., Badar-ud-Duja, S., Hussain, A., & Raheel, M. (2020). Optimal Rating Prediction in Recommender Systems. Communications in Computer and Information Science (2020) 1179 CCIS 331-339. https://doi.org/https://doi.org/10.1007/978-981-15-2810-1_32
Ayundhita, M. S., Baizal, Z. K. A., & Sibaroni, Y. (2019). Ontology-based conversational recommender system for recommending laptop. Journal of Physics: Conference Series, 1192(1). https://doi.org/10.1088/1742-6596/1192/1/012020
Baizal, Z. K. A., Widyantoro, D. H., & Maulidevi, N. U. (2017). Design of knowledge for conversational recommender system based on product functional requirements. Proceedings of 2016 International Conference on Data and Software Engineering, ICoDSE 2016. https://doi.org/10.1109/ICODSE.2016.7936151
Hasan, M., & Roy, F. (2019). An item–item collaborative filtering recommender system using trust and genre to address the cold-start problem. Big Data and Cognitive Computing, 3(3), 1–15. https://doi.org/10.3390/bdcc3030039
Ibrahim, M. E., Yang, Y., Ndzi, D. L., Yang, G., & Al-Maliki, M. (2019). Ontology-Based Personalized Course Recommendation Framework. IEEE Access, 7(c), 5180–5199. https://doi.org/10.1109/ACCESS.2018.2889635
Iswari, N. M. S., Wella, & Rusli, A. (2019). Product Recommendation for e-Commerce System based on Ontology Ni. 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), 1(August), 105–109. https://doi.org/10.1109/CREBUS.2019.8840063
Jeevamol, J., & Renumol, V. G. (2021). An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Education and Information Technologies, 26(4), 4993–5022. https://doi.org/10.1007/s10639-021-10508-0
Jin, Y., Cai, W., Chen, L., Htun, N. N., & Verbert, K. (2019). MusicBot: Evaluating critiquing-based music recommenders with conversational interaction. International Conference on Information and Knowledge Management, Proceedings, 951–960. https://doi.org/10.1145/3357384.3357923
Kumar, P., Kumar, V., & Thakur, R. S. (2019). A new approach for rating prediction system using collaborative filtering. Iran Journal of Computer Science, 2(2), 81–87. https://doi.org/10.1007/s42044-018-00028-5
Laseno, F. U. D., & Hendradjaya, B. (2019). Knowledge-Based Filtering Recommender System to Propose Design Elements of Serious Game. Proceedings of the International Conference on Electrical Engineering and Informatics, 2019-July(July), 158–163. https://doi.org/10.1109/ICEEI47359.2019.8988797
Lee, C. I., Hsia, T. C., Hsu, H. C., & Lin, J. Y. (2017). Ontology-based tourism recommendation system. 2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017, 376–379. https://doi.org/10.1109/IEA.2017.7939242
Sharma, P., & Yadav, L. (2020). Movie Recommendation System Using Item Based Collaborative Filtering. International Journal of Innovative Research in Computer Science & Technology, 8(4), 8–12. https://doi.org/10.21276/ijircst.2020.8.4.2
Tjayadi, S., & Mawardi, V. C. (2022). Laptop Recommendation Intelligent Virtual Assistant using Recurrent Neural Network with RPA for Data Scraping. Proceedings of the 2022 IEEE 7th International Conference on Information Technology and Digital Applications, ICITDA 2022. https://doi.org/10.1109/ICITDA55840.2022.9971263
Yehuda Koren, Rendle, S., & Bell, R. (2021). Recommender systems handbook. In F. Ricci, Lior Rokach, & Bracha Shapira (Eds.), Recommender Systems Handbook: Third Edition (3rd ed.). https://doi.org/10.1007/978-1-0716-2197-4_14
Yera Toledo, R., Alzahrani, A. A., & Martinez, L. (2019). A food recommender system considering nutritional information and user preferences. IEEE Access, 7, 96695–96711. https://doi.org/10.1109/ACCESS.2019.2929413
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Copyright (c) 2024 Z. K. A. Baizal, Alvian Daniswara Adhipramana Putra
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