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Musical Instruments Recommendation System Using Collaborative Filtering and KNN
Corresponding Author(s) : Alfriska Deviane Puspita
Proceedings Universitas Muhammadiyah Yogyakarta Undergraduate Conference,
Vol. 1 No. 2 (2021): Engaging Youth in Community Development to Strengthen Nation's Welfare
Abstract
Introduction – The trend of e-commerce and online shopping has offered customers more product choices, but it also resulted in information overload. Nowadays, users are equipped with technology that allows websites to automatically deliver products that they may be interested in so that they can easily locate their favorite items from enormous options. To automate the recommendation process, recommender systems are created and built. This research creates a musical instrument recommendation system based on user reviews.
Methodology/Approach – In this paper, we design and implement a recommendation system that combines the k-Nearest Neighbor (kNN) algorithm with a collaborative filtering framework. Collaborative filtering is chosen in this case because of its capability of providing new information to users by collecting information that has been obtained from the other users. Furthermore, kNN is considered as a suitable combination in this case since this method is relatively simple and able to find the similarity of objects being compared.
Findings – To evaluate this study, the recommendation results are evaluated using the Root Mean Square Error (RMSE) calculation method, and the RMSE result obtained is 0.8734 for schema that divides dataset into 70% data train and 30% dataset using KNNWith Means with pearson measurements, and the MAE (Mean Absolute Error) result obtained is 0.5998 with schema 60% data train and 40% data test using KNNBasic algorithm with cosine similarity.
Originality/ Value/ Implication – We present experimental results of consolidating the kNN algorithm in the collaborative filtering framework using Amazon’s musical instrument dataset. Furthermore, we can see that kNN together with a collaborative filtering algorithm performs a satisfactory outcome.
Keywords
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- Ahuja, R., Solanki, A., & Nayyar, A. (2019). Movie recommender system using k-means clustering and k-nearest neighbor. Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering, Confluence 2019,263–268. https://doi.org/10.1109/CONFLUENCE.2019.877696
- Arai, K., Kapoor, S., & Bhatia, R. (Eds.). (2020). Intelligent Computing. In Intelligent Computing Proceedings of the 2020 Computing Conference, (Vol. 1228). Springer International Publishing. https://doi.org/10.1007/978-3-030-52249-0
- Baskota, A., & Ng, Y. K. (2018). A graduate school recommendation system using the multi-class support vector machine and KNN approaches. Proceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018, 277–284. https://doi.org/10.1109/IRI.2018.00050
- Erlangga, E., & Sutrisno, H. (2020). Sistem Rekomendasi Beauty Shop Berbasis Collaborative Filtering. EXPERT: Jurnal Manajemen Sistem Informasi Dan Teknologi, 10(2),47. https://doi.org/10.36448/jmsit.v10i2.1611
- Gupta, M., Thakkar, A., Aashish, Gupta, V., & Rathore, D. P. S. (2020). Movie Recommender System Using Collaborative Filtering. Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, Icesc, 415–420. https://doi.org/10.1109/ICESC48915.2020.9155879
- Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). Applying Collaborative Filtering to Usenet News. Communications of the ACM, 40(3), 77–87. https://doi.org/10.1145/245108.245126
- Li, G., & Zhang, J. (2018). Music personalized recommendation system based on improved KNN algorithm. Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018, Iaeac, 777–781. https://doi.org/10.1109/IAEAC.2018.8577483
- Muneer, A., Fati, S. M., & Al-Ghobari, M. (2021). Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN. Computers, Materials & Continua. https://doi.org/10.32604/cmc.2021.016348
References
Ahuja, R., Solanki, A., & Nayyar, A. (2019). Movie recommender system using k-means clustering and k-nearest neighbor. Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering, Confluence 2019,263–268. https://doi.org/10.1109/CONFLUENCE.2019.877696
Arai, K., Kapoor, S., & Bhatia, R. (Eds.). (2020). Intelligent Computing. In Intelligent Computing Proceedings of the 2020 Computing Conference, (Vol. 1228). Springer International Publishing. https://doi.org/10.1007/978-3-030-52249-0
Baskota, A., & Ng, Y. K. (2018). A graduate school recommendation system using the multi-class support vector machine and KNN approaches. Proceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018, 277–284. https://doi.org/10.1109/IRI.2018.00050
Erlangga, E., & Sutrisno, H. (2020). Sistem Rekomendasi Beauty Shop Berbasis Collaborative Filtering. EXPERT: Jurnal Manajemen Sistem Informasi Dan Teknologi, 10(2),47. https://doi.org/10.36448/jmsit.v10i2.1611
Gupta, M., Thakkar, A., Aashish, Gupta, V., & Rathore, D. P. S. (2020). Movie Recommender System Using Collaborative Filtering. Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, Icesc, 415–420. https://doi.org/10.1109/ICESC48915.2020.9155879
Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). Applying Collaborative Filtering to Usenet News. Communications of the ACM, 40(3), 77–87. https://doi.org/10.1145/245108.245126
Li, G., & Zhang, J. (2018). Music personalized recommendation system based on improved KNN algorithm. Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018, Iaeac, 777–781. https://doi.org/10.1109/IAEAC.2018.8577483
Muneer, A., Fati, S. M., & Al-Ghobari, M. (2021). Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN. Computers, Materials & Continua. https://doi.org/10.32604/cmc.2021.016348