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A Strategy for Neighborhood Selection in Collaborative Filtering-based Recommender Systems
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  • Journal title : Journal of KIISE
  • Volume 42, Issue 11,  2015, pp.1380-1385
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2015.42.11.1380
 Title & Authors
A Strategy for Neighborhood Selection in Collaborative Filtering-based Recommender Systems
Lee, Soojung;
Collaborative filtering is one of the most successfully used methods for recommender systems and has been utilized in various areas such as books and music. The key point of this method is selecting the most proper recommenders, for which various similarity measures have been studied. To improve recommendation performance, this study analyzes problems of existing recommender selection methods based on similarity and presents a method of dynamically determining recommenders based on the rate of co-rated items as well as similarity. Examination of performance with varying thresholds through experiments revealed that the proposed method yielded greatly improved results in both prediction and recommendation qualities, and that in particular, this method showed performance improvements with only a few recommenders satisfying the given thresholds.
collaborative filtering;recommender system;similarity measure;nearest neighbor;
 Cited by
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D. Arotaritei and S. Mitra, "Web Mining: A Survey in the Fuzzy Framework," Fuzzy Sets and Systems, Vol. 148, No. 1, pp. 5-19, 2004. crossref(new window)

D. Jannach, Z. Karakaya, and F. Gedikli, "Accuracy Improvements for Multi-criteria Recommender Systems," Proc. of the ACM Conf. Electronic Commerce, pp. 674-689, 2012.

G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions," IEEE Trans. Knowledge & Data Engineering, Vol. 17, No. 6, pp. 734-749, 2005. crossref(new window)

H. J. Ahn, "A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-starting Problem," Information Sciences, Vol. 178, No. 1, pp. 37-51, 2008. crossref(new window)

B. Jeong, J. Lee, and H. Cho, "Improving Memory-based Collaborative Filtering via Similarity Updating and Prediction Modulation," Information Sciences, Vol. 180, No. 5, pp. 602-612, 2010. crossref(new window)

H. Liu, et al., "A New User Similarity Model to Improve the Accuracy of Collaborative Filtering," Knowledge-Based Systems, Vol. 56, pp. 156-166, 2014. crossref(new window)

S. Lee, "A Rank-based Similarity Measure for Collaborative Filtering Systems," Journal of KACE, Vol. 14, No. 5, pp. 97-104, 2011. (in Korean)

P. Resnick, et al., "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Proc. of the ACM Conf. Computer Supported Cooperative Work, pp. 175-186, 1994.

J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, "A Collaborative Filtering Approach to Mitigate the New User Cold Start Problem," Knowledge-Based Systems, Vol. 26, pp. 225-238, 2011.

G. Koutrica, B. Bercovitz, and H. Garcia, "FlexRecs: Expresing and Combining Flexible Recommendations," Proc. of the ACM SIGMOD Int'l Conf. on Management of data, pp. 745-758, 2009.

MovieLens, [Online]. Available:

M. Gao, Z. Wu, and F. Jiang, "Userrank for Itembased Collaborative Filtering Recommendation," Information Processing Letters, Vol. 111, No. 9, pp. 440-446, 2011. crossref(new window)