Article Sidebar

Download
PDF
Statistic
Read Counter : 572 Download : 419

Downloads

Download data is not yet available.

Main Article Content

Abstract

In the current digital era where content consumption via streaming platforms is increasing, the need for accurate recommendation systems is becoming increasingly important, especially in the animation industry. This research focuses on implementing a recommendation system that can help viewers easily navigate the abundance of content. By comparing collaborative filtering and content-based filtering methods, this research attempts to find the optimal approach for providing anime recommendations. From the results of A/B testing and further analysis, it was found that Collaborative Filtering was effective in providing recommendations based on similar interests between users. On the other hand, content-based filtering offers the advantage of personalizing recommendations based on content characteristics. Additionally, integrating these techniques into mobile applications will enrich the user experience, allowing them to receive recommendations more quickly and interactively. With these findings, this research contributes to the development of more intuitive and responsive recommendation systems, driving the growth of the anime streaming industry by increasing user satisfaction and retention.

Keywords

Collaborative Filtering Content-Based Filtering Anime Recommendation Systems User Preferences

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Institute of Electrical and Electronics Engineers and Hindusthan Institute of Technology, Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC 2020) : 02-04, July 2020. 2020.
  2. S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-based movie recommendation system using genre correlation,” in Smart Innovation, Systems and Technologies, Springer Science and Business Media Deutschland GmbH, 2019, pp. 391–397. doi: 10.1007/978-981-13-1927-3_42.
  3. S. K. Raghuwanshi and R. K. Pateriya, “Collaborative Filtering Techniques in Recommendation Systems,” in Data, Engineering and Applications: Volume 1, vol. 1, Springer Singapore, 2019, pp. 11–21. doi: 10.1007/978-981-13-6347-4_2.
  4. J. Chen, H. Zhang, X. He, L. Nie, W. Liu, and T. S. Chua, “Attentive collaborative filtering: Multimedia recommendation with item-And component-level attention,” in SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, Inc, Aug. 2017, pp. 335–344. doi: 10.1145/3077136.3080797.
  5. N. Nassar, A. Jafar, and Y. Rahhal, “A novel deep multi-criteria collaborative filtering model for recommendation system ✩,” vol. 187, p. 104811, 2020, doi: 10.1016/j.knosys.
  6. Institute of Electrical and Electronics Engineers and PPG Institute of Technology, Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES 2020) : 10-12, June 2020. 2020.
  7. Institute of Electrical and Electronics Engineers. Madras Section and Institute of Electrical and Electronics Engineers, 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). 2019.
  8. T. Badriyah et al., “Konferensi Nasional Sistem Informasi 2018 STMIK Atma Luhur Pangkalpinang,” 2018.
  9. D. K. Chae, S. C. Lee, S. Y. Lee, and S. W. Kim, “On identifying k-nearest neighbors in neighborhood models for efficient and effective collaborative filtering,” Neurocomputing, vol. 278, pp. 134–143, Feb. 2018, doi: 10.1016/j.neucom.2017.06.081.
  10. C. Yang, L. Bai, C. Zhang, Q. Yuan, and J. Han, “Bridging collaborative filtering and semi-supervised learning: A neural approach for POI recommendation,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Aug. 2017, pp. 1245–1254. doi: 10.1145/3097983.3098094.
  11. Amity University and Institute of Electrical and Electronics Engineers, Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering : Confluence 2019 : 10-11 January 2019, Uttar Pradesh, India. 2019.
  12. C. E. Berbague, N. E. Karabdji, and H. Seridi, 2018 International Symposium on Programming and Systems (ISPS). IEEE, 2018.
  13. A. Hernando, J. Bobadilla, and F. Ortega, “A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model,” Knowl Based Syst, vol. 97, pp. 188–202, Apr. 2016, doi: 10.1016/j.knosys.2015.12.018.
  14. M. J. Mokarrama, S. Khatun, and M. S. Arefin, “A content-based recommender system for choosing universities,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 28, no. 4, pp. 2128–2142, Jul. 2020, doi: 10.3906/ELK-1911-37.
  15. K. Wahyudi, J. Latupapua, R. Chandra, and A. S. Girsang, “Hotel content-based recommendation system,” in Journal of Physics: Conference Series, Institute of Physics Publishing, May 2020. doi: 10.1088/1742-6596/1485/1/012017.