Optimization of the Similarity Measure for User-based Collaborative Filtering Systems

사용자 기반의 협력필터링 시스템을 위한 유사도 측정의 최적화

  • Received : 2015.10.13
  • Accepted : 2016.01.20
  • Published : 2016.01.30

Abstract

Measuring similarity in collaborative filtering-based recommender systems greatly affects system performance. This is because items are recommended from other similar users. In order to overcome the biggest problem of traditional similarity measures, i.e., data sparsity problem, this study suggests a new similarity measure that is the optimal combination of previous similarity and the value reflecting the number of co-rated items. We conducted experiments with various conditions to evaluate performance of the proposed measure. As a result, the proposed measure yielded much better performance than previous ones in terms of prediction qualities, specifically the maximum of about 7% improvement over the traditional Pearson correlation and about 4% over the cosine similarity.

협력 필터링 기반의 추천시스템에서 유사도 측정은 시스템의 성능에 큰 영향을 미치는데, 이는 유사한 다른 사용자들로부터 항목을 추천받기 때문이다. 본 연구에서는 전통적인 유사도 측정 방법의 가장 큰 문제인 데이터 희소성을 극복하기 위해, 기존의 유사도 측정값과 공통평가항목수의 반영값을 최적으로 결합하는 새로운 유사도 측정방식을 제안한다. 제안 방식의 성능 평가를 위해 다양한 조건으로 실험한 결과 기존 방식들보다 우수한 예측 정확도를 나타냈으며, 구체적으로 전통적인 피어슨 상관보다 최대 약 7%, 코사인 유사도보다는 최대 약 4% 향상된 결과를 보였다.

Keywords

References

  1. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge & Data Engineering, 17(6), 734-749. https://doi.org/10.1109/TKDE.2005.99
  2. Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 4.
  3. 김지혜.박두순 (2006). 연관규칙과 협업적 필터링을 이용한 상품 추천 시스템 개발. 컴퓨터교육학회논문지, 9(1), 1-10.
  4. Bobadilla, J., Ortega, F., Hernando, A., & Bernal, J. (2011). A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems, 26, 225-238.
  5. Jamali, M., & Ester, M. (2009, June). TrustWalker: a random walk model for combining trust-based and item-based recommendation. In Prococeedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 397-406). ACM.
  6. Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems, 56, 156-166.
  7. Bobadilla, J., Ortega, F., Hernando, A., & Alcal, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-Based Systems, 24(8), 1310-1316.
  8. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994, October). GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Cconference on Computer Supported Cooperative Work (pp. 175-186). ACM.
  9. Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5-53. https://doi.org/10.1145/963770.963772
  10. Koutrica, G., Bercovitz, B., & Garcia-Molina, H. (2009, June). FlexRecs: expresing and combining flexible recommendations. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (pp. 745-758). ACM.
  11. Mitchell, T. M. (2010). Machine Learning. IL: McGraw Hill.
  12. Hwang, C. S., Su, Y. C., & Tseng, K. C. (2010). Using genetic algorithms for personalized recommendation. In Computational Collective Intelligence, Technologies and Applications (pp. 104-112). Springer Berlin Heidelberg.
  13. Alander, J. T. (1992, May). On optimal population size of genetic algorithms. In CompEuro'92. 'Computer Systems and Software Engineering', Proceedings. (pp. 65-70). IEEE.
  14. Goldberg, K., Roeder, T., Gupta, D., & Perkins, C. (2001). Eigentaste: a constant time collaborative filtering algorithm. Information Retrieval, 4(2), 133-151. https://doi.org/10.1023/A:1011419012209
  15. Anand, D., & Bharadwaj, K. K. (2010, July). Adaptive user similarity measures for recommender systems: a genetic programming approach. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Vol. 8, pp. 121-125). IEEE.
  16. 이수정 (2011). 협력 필터링 시스템을 위한 순위 기반의 유사도 척도. 컴퓨터교육학회논문지, 14(5), 97-104.