Araştırma Makalesi
Yıl 2021,
Cilt: 2 Sayı: 2, 36 - 42, 29.06.2021
Öz
Kaynakça
- Ahuja, R., Solanki, A., & Nayyar, A. (2019, January). Movie recommender system using K-Means clustering and K-Nearest Neighbor. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 263-268). IEEE.
- Bobadilla, J., Ortega, F., Hernando, A., & Alcalá, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-based systems, 24(8), 1310-1316.
- Ekstrand, M. D., Harper, F. M., Willemsen, M. C., & Konstan, J. A. (2014, October). User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 161-168).
- Ekstrand, M. D., Kluver, D., Harper, F. M., & Konstan, J. A. (2015, September). Letting users choose recommender algorithms: An experimental study. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 11-18).
- Elahi, M., Ricci, F., & Rubens, N. (2016). A survey of active learning in collaborative filtering recommender systems. Computer Science Review, 20, 29-50.
- Koren, Y. (2009). The bellkor solution to the netflix grand prize. Netflix prize documentation, 81(2009), 1-10.
- Kumar, M., Yadav, D. K., Singh, A., & Gupta, V. K. (2015). A movie recommender system: Movrec. International Journal of Computer Applications, 124(3).
- Wang, K., Peng, H., Jin, Y., Sha, C., & Wang, X. (2016). Local weighted matrix factorization for top-n recommendation with implicit feedback. Data Science and Engineering, 1(4), 252-264.
- Wang, Z., Yu, X., Feng, N., & Wang, Z. (2014). An improved collaborative movie recommendation system using computational intelligence. Journal of Visual Languages & Computing, 25(6), 667-675.
- Xia, P., Zhang, L., & Li, F. (2015). Learning similarity with cosine similarity ensemble. Information Sciences, 307, 39-52.
- Zhang, J., Peng, Q., Sun, S., & Liu, C. (2014). Collaborative filtering recommendation algorithm based on user preference derived from item domain features. Physica A: Statistical Mechanics and its Applications, 396, 66-76.
- URL-1: https://www.amazon.science/the-history-of-amazons-recommendation-algorithm [Erişim Tarihi: 12.03.2021]
- URL-2: https://developers.google.com/machine-learning/recommendation/collaborative/matrix [Erişim Tarihi: 12.03.2021]
- URL-3: https://grouplens.org/datasets/movielens/ [Erişim Tarihi: 12.03.2021]
Yıl 2021,
Cilt: 2 Sayı: 2, 36 - 42, 29.06.2021
Öz
Öneri sistemleri kullanıcılara en kısa yoldan istedikleri öğeye ulaşmasını sağlamayı amaçlar. Kullanıcının geçmiş davranışları, öğelerin içerikleri ve daha birçok parametrenin dâhil edildiği birçok farklı öneri yaklaşımı bulunmaktadır. Bunlar kullanıcı memnuniyeti, hız, performans gibi alanlarda farklı sonuçlar sunmaktadır. Kullanıcıya yapılan önerinin en iyi ve en kısa yoldan yapılması kullanıcının memnuniyetini artırır. Bu sebeple günümüzde birçok şirket, öneri algoritmalarını performans ve doğruluk bakımından geliştirmenin yollarını aramaktadır. Bu çalışmada MovieLens veri kümesini kullanılarak üç farklı öneri sistemi geliştirilmiş performansları karşılaştırılmıştır.
Anahtar Kelimeler
Kaynakça
- Ahuja, R., Solanki, A., & Nayyar, A. (2019, January). Movie recommender system using K-Means clustering and K-Nearest Neighbor. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 263-268). IEEE.
- Bobadilla, J., Ortega, F., Hernando, A., & Alcalá, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-based systems, 24(8), 1310-1316.
- Ekstrand, M. D., Harper, F. M., Willemsen, M. C., & Konstan, J. A. (2014, October). User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 161-168).
- Ekstrand, M. D., Kluver, D., Harper, F. M., & Konstan, J. A. (2015, September). Letting users choose recommender algorithms: An experimental study. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 11-18).
- Elahi, M., Ricci, F., & Rubens, N. (2016). A survey of active learning in collaborative filtering recommender systems. Computer Science Review, 20, 29-50.
- Koren, Y. (2009). The bellkor solution to the netflix grand prize. Netflix prize documentation, 81(2009), 1-10.
- Kumar, M., Yadav, D. K., Singh, A., & Gupta, V. K. (2015). A movie recommender system: Movrec. International Journal of Computer Applications, 124(3).
- Wang, K., Peng, H., Jin, Y., Sha, C., & Wang, X. (2016). Local weighted matrix factorization for top-n recommendation with implicit feedback. Data Science and Engineering, 1(4), 252-264.
- Wang, Z., Yu, X., Feng, N., & Wang, Z. (2014). An improved collaborative movie recommendation system using computational intelligence. Journal of Visual Languages & Computing, 25(6), 667-675.
- Xia, P., Zhang, L., & Li, F. (2015). Learning similarity with cosine similarity ensemble. Information Sciences, 307, 39-52.
- Zhang, J., Peng, Q., Sun, S., & Liu, C. (2014). Collaborative filtering recommendation algorithm based on user preference derived from item domain features. Physica A: Statistical Mechanics and its Applications, 396, 66-76.
- URL-1: https://www.amazon.science/the-history-of-amazons-recommendation-algorithm [Erişim Tarihi: 12.03.2021]
- URL-2: https://developers.google.com/machine-learning/recommendation/collaborative/matrix [Erişim Tarihi: 12.03.2021]
- URL-3: https://grouplens.org/datasets/movielens/ [Erişim Tarihi: 12.03.2021]
Toplam 14 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Yapay Zeka |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 29 Haziran 2021 |
| Gönderilme Tarihi | 15 Mart 2021 |
| Kabul Tarihi | 9 Haziran 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 2 Sayı: 2 |
