Dynamic k Neighbor Selection for Collaborative Filtering

Year 2018, Volume: 19 Issue: 2, 303 - 315, 31.03.2018

Halil Zeybek Cihan Kaleli

https://doi.org/10.18038/aubtda.346407

Cited By: 3

Abstract

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

Keywords

k-nearest-neighbor; Collaborative filtering; Dynamic k; Accuracy.

References

• [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.
• [7] Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009; 2009: 4.
• [8] Adomavicius G, Manouselis N, Kwon Y. Multi-criteria recommender systems. In: Recommender Systems Handbook. Springer US, 2011. pp.769-803.
• [9] 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.
• [10] 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.
• [11] 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.
• [12] Delgado, J., Ishii, N.: Memory-based weighted majority prediction for recommender systems. In: Proc. of the ACM SIGIR’99 Workshop on Recommender Systems (1999)
• [13] 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)
• [14] Kaleli C. An entropy-based neighbor selection approach for collaborative filtering. Knowledge-based Systems, 2014. pp. 273-280.
• [15] J. Herlocker, J.A. Konstan, J. Riedl, An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms, Inform. Retrieval 5 (4) (2002) 287–310.
• [16] T.-H. Kim, S.-B. Yang, An effective threshold-based neighbor selection in collaborative filtering, in: Proceedings of the 29th European conference on IR Research. ECIR’07, Springer-Verlag, Berlin, Heidelberg, 2007, pp. 712–715.
• [17] Z. Liang, X. Bo, G. Jun, An approach of selecting right neighbors for collaborative filtering, in: Proceedings of the Innovative Computing, Information, and Control (ICICIC), 2009 Fourth International Conference on, 2009, pp. 1057–1060.
• [18] Y. Koren, Factor in the neighbors: scalable and accurate collaborative filtering, ACM Trans. Knowl. Discovery Data 4 (1) (2010) 1–24.
• [19]B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, in: Proceedings of the 10th International Conference on World Wide Web, 2001, pp. 285–295.
• [20] C.C. Aggarwal, Recommender Systems: The Textbook. Springer,2016.
• [21] B.Sarwar, J. Konstan, A. Borchers, J. Herlocker, B. Miller, J.Riedl, Using filtering agents to improve prediction quality in the grouplens research collaborative filtering systems, Proceedings of 1998 Conference on Computer Supported Collaborative Work, 1998
• [22] U. Shardanand, P. Maes, Social information filtering, Proceedings of ACH CHI’95 Conference on Human Factors in Computing Systems, 1995, pp. 210-217