Weighting impact on hybrid user-based and item-based method for movie recommendation system
Abstract
The rapid development of technology means that information is becoming more abundant and diverse, including in the movie industry. The number of movies each year can reach tens of thousands with various genres, making it difficult for potential viewers to choose a movie that suits their interests. One solution to the problem is the existence of a recommendation system that can provide movie recommendations based on information on movies that have been watched previously. Collaborative Filtering is a widely used approach in recommendation systems. Collaborative Filtering offers recommendations based on the similarity between users for User-based methods and the similarity between items for Item-based methods. However, the similarity value can be high for data with high dispersion even though only one item is in common. The Hybrid method can be a solution to overcome this by combining User-based and Item-based methods and adding genre information from the items. The final prediction result is obtained from the prediction results of all methods combined using linear combination. The combination is done by giving weight to each method and then summarising it. This study aims to determine the impact of weighting variations on Hybrid User-based and Item-based Collaborative Filtering methods. The results obtained from this study show that More Dominant User-based and Very Dominant User-based weightings are superior to other weightings because they show good performance for a smaller list of recommendations.
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References
[2] Thakker, U., Ruhi Patel, and Shah, M., 2021, A comprehensive analysis on movie recommendation system employing collaborative filtering. Multimedia Tools and Applications 80, pp. 28647-28672, doi: https://doi.org/10.1007/s11042-021-10965-2.
[3] Ifada, N., Rahman, T. F., and Sophan, M. K., 2020, Comparing Collaborative Filtering and Hybrid based Approaches for Movie Recommendation. The 6th Information Technology International Seminar (ITIS), Surabaya, pp. 219-223, doi: https://doi.org/10.1109/ITIS50118.2020.9321014.
[4] Dietz, A. E., Personalizing Talent Development Utilizing A Network Framework. Thesis. Monterey, CA: Naval Postgraduate School, 2021.
[5] Barman, S. D., Hasan, M., and Roy, F., 2019, A genre-based item-item collaborative filtering: facing the cold-start problem. The 2019 8th International Conference on Software and Computer Applications, Penang, Malaysia, pp. 258-262, doi: https://doi.org/10.1145/3316615.3316732.
[6] Ardiansyah, R., Saputra, B. D., and Bianto, M. A., 2023, Sistem Rekomendasi Buku Perpustakaan Sekolah menggunakan Metode Content-Based Filtering. Jurnal Computer Science and Information Technology (CoSciTech), 4(2), pp. 510-517, doi: https://doi.org/10.37859/coscitech.v4i2.5131.
[7] Kanmani, R. S. A., Surendiran, B., and Ibrahim, S. P. S., 2021, Recency augmented hybrid collaborative movie recommendation system. International Journal of Information Technology and Management, 13(5), pp. 1829-1836, doi: https://doi.org/10.1007/s41870-021-00769-w.
[8] Pirasteh, P., Bouguelia, M.-R., and Santosh, K. C., 2021, Personalized recommendation: an enhanced hybrid collaborative filtering. Advances in Computational Intelligence 1, p. 1, doi: https://doi.org/10.1007/s43674-021-00001-z.
[9] Nudrat, S., Khan, H. U., Iqbal, S., Talha, M. M., Alarfaj, F. K., and Almusallam, N., 2022, Users’ Rating Predictions Using Collaborating Filtering Based on Users and Items Similarity Measures. Computational Intelligence and Neuroscience, 2022(1), p. 2347641, doi: https://doi.org/10.1155/2022/2347641.
[10] Harper, F. and Konstan, J., 2015, The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems, 5(4), pp. 1-19, doi: https://doi.org/10.1145/2827872.
[11] Fajriansyah, M., Adikara, P. P., and Widodo, A. W., 2021, Sistem Rekomendasi Film Menggunakan Content Based Filtering. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 5(6), pp. 2188-2199,
[12] Lubis, Y. I., Napitupulu, D. J., and Dharma, A. S., 2020, Implementasi Metode Hybrid Filtering (Collaborative dan Content-based) untuk Sistem Rekomendasi Pariwisata. The 12th Conference on Information Technology and Electrical Engineering, Yogyakarta, Indonesia, pp. 28-35,
[13] Tong, Y., Luo, Y., Zhang, Z., Sadiq, S., and Cui, P., 2019, Collaborative generative adversarial network for recommendation systems. The 2019 IEEE 35th international conference on data engineering workshops (ICDEW), Macao, China, pp. 161-168, doi: https://doi.org/10.1109/ICDEW.2019.00-16.
[14] Ifada, N., Putri, N. F. D., and Sophan, M. K., 2020, Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System. Rekayasa, 13(3), pp. 234-239, doi: https://doi.org/10.21107/rekayasa.v13i3.8545.
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