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Dergiler / Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik / 2018 / Cilt: 19 - Sayı: 2

DYNAMIC k NEIGHBOR SELECTION FOR COLLABORATIVE FILTERING

Cihan KALELİ
Halil ZEYBEK
Sayfa
303–315
Yayın Tarihi
2022-10-11
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Empirical outcomes onmovie based dataset show that using the dynamic k values during the k-nn algorithm leads to more favorablerecommendations compared to a constant k value.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Collaborative filtering is a commonly used method to reduce information overload.
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Temel Bulgular & Veri:For thispurpose, we generate a model that determines k values for each user or item at the off-line time.
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Sonuç & Katkı:Empirical outcomes onmovie based dataset show that using the dynamic k values during the k-nn algorithm leads to more favorablerecommendations compared to a constant k value.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Collaborative filtering is a commonly used method to reduce information overload. It is widely used in recommendationsystems due to its simplicity. In traditional collaborative filtering, recommendations are produced based on similaritiesamong users/items. In this approach, the most correlated k neighbors are determined, and a prediction is computed for eachuser/item by utilizing this neighborhood. During recommendation process, a predefined k value as a number of neighbors isusedfor prediction processes. In this paper, we analyze the effect of selecting different k values for each user or item. For thispurpose, we generate a model that determines k values for each user or item at the off-line time. Empirical outcomes onmovie based dataset show that using the dynamic k values during the k-nn algorithm leads to more favorablerecommendations compared to a constant k value.

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Bu Makaleye Atıf Yap

KALELİ, C., ZEYBEK, H. (2022). DYNAMIC k NEIGHBOR SELECTION FOR COLLABORATIVE FILTERING. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 19 - Sayı: 2, 303–315. https://ajindex.com/articles/438234

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Kaynakça (13)

  1. White T. Hadoop the definitive guide. O'Reilly US, 2015
  2. Ricci F, Rokach L, Shapira B. Recommender Systems Handbook. Springer US, 2011
  3. Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005; 17.6: pp. 734-749
  4. Bilge A, Yargıç A. Improving accuracy of muti-criteria collaborative filtering by normalizing user ratings. Anadolu University Journal of Science and Technology a- applied science and engineering, 2017; 18.1: pp. 225-237 [5] Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man and cybernetics, 1985; 15.4. pp. 580-585 [6] Herlocker JL, Konstan JA, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1999, pp. 230-237
  5. Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009; 4
  6. Adomavicius G, Manouselis N, Kwon Y. Multi-criteria recommender systems. In: Recommender Systems Handbook. Springer US, 2011; pp.769-803
  7. Jannach D, Karakaya Z, Gedikli F. Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM Conference on Electronic Commerce. ACM, 2012; pp. 674-689
  8. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. ACM, 1994; pp. 175-186
  9. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the 14th Annual Conf. on Uncertainty in Artificial Intelligence, 1998; pp. 43–52
  10. Delgado, J., Ishii, N.: Memory-based weighted majority prediction for recommender systems. In: Proc. of the ACM SIGIR’99 Workshop on Recommender Systems (1999
  11. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD’08: Proceeding of the 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 426–434. ACM, New York, NY, USA, 2008
  12. Kaleli C. An entropy-based neighbor selection approach for collaborative filtering. Knowledgebased Systems, 2014; pp. 273-280
  13. Herlocker J, Konstan JA, Riedl J. An empirical analysi