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A Strategy for Neighborhood Selection in Collaborative Filtering-based Recommender Systems

협력 필터링 기반의 추천 시스템을 위한 이웃 선정 전략

  • 이수정 (경인교육대학교 컴퓨터교육과)
  • Received : 2015.06.29
  • Accepted : 2015.08.26
  • Published : 2015.11.15

Abstract

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.

협력 필터링은 가장 성공적으로 사용되는 추천 시스템의 방법으로서, 서적, 음악 등 다방면의 상업 시스템에서 활용되어왔다. 이러한 방법의 핵심은 사용자에게 가장 적합한 추천인들을 선정하는 것인데, 이를 위하여 다양한 유사도 측정 방법이 연구되었다. 본 연구에서는 추천 성능의 향상을 위하여 기존의 유사도 값에 근거한 추천인 선정의 문제점을 파악하고 이의 개선책으로서 유사도 값과 공통평가항목수의 비율을 기준으로 하여 가변적으로 추천인을 결정하는 방법을 제시한다. 실험을 통하여 다양한 기준값에 대해 성능 변화를 관찰한 결과, 예측 성능과 추천 성능의 두 측면 모두에서 제안 방법이 매우 향상된 결과를 가져왔으며, 특히 주어진 기준값을 만족하는 추천인 수가 적을 때에도 향상된 성능 결과를 보였다.

Keywords

References

  1. 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. https://doi.org/10.1016/j.fss.2004.03.003
  2. 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.
  3. 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. https://doi.org/10.1109/TKDE.2005.99
  4. 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. https://doi.org/10.1016/j.ins.2007.07.024
  5. 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. https://doi.org/10.1016/j.ins.2009.10.016
  6. 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. https://doi.org/10.1016/j.knosys.2013.11.006
  7. S. Lee, "A Rank-based Similarity Measure for Collaborative Filtering Systems," Journal of KACE, Vol. 14, No. 5, pp. 97-104, 2011. (in Korean)
  8. 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.
  9. 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.
  10. 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.
  11. MovieLens, [Online]. Available: http://movielens.umn.edu
  12. M. Gao, Z. Wu, and F. Jiang, "Userrank for Itembased Collaborative Filtering Recommendation," Information Processing Letters, Vol. 111, No. 9, pp. 440-446, 2011. https://doi.org/10.1016/j.ipl.2011.02.003