Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Fuzzy Clustering with Genre Preference for Collaborative Filtering

  • Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
  • Received : 2020.03.31
  • Accepted : 2020.05.12
  • Published : 2020.05.29
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Abstract

The scalability problem inherent in collaborative filtering-based recommender systems has been an issue in related studies during past decades. Clustering is a well-known technique for handling this problem, but has not been actively studied due to its low performance. This paper adopts a clustering method to overcome the scalability problem, inherent drawback of collaborative filtering systems. Furthermore, in order to handle performance degradation caused by applying clustering into collaborative filtering, we take two strategies into account. First, we use fuzzy clustering and secondly, we propose and apply a similarity estimation method based on user preference for movie genres. The proposed method of this study is evaluated through experiments and compared with several previous relevant methods in terms of major performance metrics. Experimental results show that the proposed demonstrated superior performance in prediction and rank accuracies and comparable performance to the best method in our experiments in recommendation accuracy.

협력 필터링 기반의 추천 시스템에 내재된 확장성 문제는 지난 수십년간 관련 연구의 이슈가 되어 왔다. 클러스터링은 이 문제를 해결하는 유명한 기술인데 낮은 성능으로 인하여 활발히 연구되어 오진 않았다. 본 논문에서는 협력 필터링 시스템의 고질적인 단점인 확장성 문제를 극복하기 위하여 클러스터링 기법을 채택하였다. 또한 클러스터링을 적용함으로 인해 초래되는 성능저하 문제를 개선하기 위해, 두 가지 전략을 사용하였는데, 첫째는 퍼지 클러스터링이며, 둘째는 영화 장르에 대한 사용자 선호도에 기반한 유사도 측정 방법을 제안하고 이를 적용하였다. 본 연구에서의 제안 방법을 기존의 여러 관련 방법들과 비교 실험을 통해 다양한 주요 성능 척도에 의거하여 평가하였는데, 실험 결과 제안 방법은 예측과 순위 정확도 측면에서 더 우수한 성능을 보였고, 추천 정확도 측면에서는 실험 대상 중 최상의 방법과 대등한 성능을 나타냈다.

Keywords

References

  1. X. Su and T.M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, 2009. DOI:10.1155/2009/421425
  2. M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, "Evaluating Collaborative Filtering Recommender Algorithms: A Survey," IEEE Access, Vol. 6, pp. 74003-74024, 2018. DOI: 10.1109/ACCESS.2018.2883742
  3. J. Gupta and J. Gadge, "Performance Analysis of Recommendation System based on Collaborative Filtering and Demographics," International Conference on Communication Information & Computing Technology, pp. 1-6, 2015. DOI: 10.1109/ICCICT.2015.7045675
  4. M. Aamir and M. Bhusry, "Recommendation System: State of the Art Approach," International Journal Computer Applications, Vol. 120, No. 12, pp. 25-32, 2015. DOI: 10.5120/21281-4200
  5. C. F. Tsai and C. Hung, "Cluster Ensembles in Collaborative Filtering Recommendation," Applied Soft Computing, Vol. 12, pp. 1417-1425, 2012. DOI: 10.1016/j.asoc.2011.11.016
  6. M. Nilashi, D. Jannach, O. Ibrahim, and N. Ithnin, "Clusteringand Regression-based Multi-criteria Collaborative Filtering with Incremental Updates," Information Sciences, Vol. 293, pp. 235-250, 2015. DOI: 10.1016/j.ins.2014.09.012
  7. H. Ye, "A Personalized Collaborative Filtering Recommendation using Association Rules Mining and Self-organizing Map," Journal of Software, Vol. 6, No. 4, 2011. DOI: 10.4304/jsw.6.4.732-739
  8. C. L. Liao and S. J. Lee, "A Clustering based Approach to Improving the Efficiency of Collaborative Filtering Recommendation," Electronic Commerce Research and Applications, Vol. 18, pp. 1-9, 2016. DOI: 10.1016/j.elerap.2016.05.001
  9. H. Koohi and K. Kiani, "User based Collaborative Filtering using Fuzzy C-means," Measurement, Vol. 91, pp. 134-139, 2016. DOI: 10.1016/j.measurement.2016.05.058
  10. H. Shivhare, A. Gupta, and S. Sharma, "Recommender System using Fuzzy C-means Clustering and Genetic Algorithm based Weighted Similarity Measure," Proceedings of International Conference on Computer, Communication and Control, pp. 1-8, 2015. DOI: 10.1109/IC4.2015.7375707
  11. C. Birtolo and D. Ronca, "Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and Trust," Expert Systems with Applications, Vol. 40, No. 17, pp. 6997-7009, 2013. DOI: 10.1016/j.eswa.2013.06.022
  12. S. Fremal and F. Lecron, "Weighting Strategies for a Recommender System using Item Clustering based on Genres," Expert Systems With Applications, Vol. 77, No. 1, pp. 105-113, 2017. DOI: 10.1016/j.eswa.2017.01.031
  13. M. K. Najafabadi, M. N.Mahrin, S. Chuprat, and H. M. Sarkan, "Improving the Accuracy of Collaborative Filtering Recommendations using Clustering and Association Rules Mining on Implicit Data," Computers in Human Behavior, Vol. 67, pp. 113-128, 2017. DOI: 10.1016/j.chb.2016.11.010
  14. M. Al-Shamri and K. Bharadwaj, "Fuzzy-genetic Approach to Recommender Systems based on a Novel Hybrid User Model," Expert Systems with Applications, Vol. 35, No. 3, pp. 1386-1399, 2008. DOI: 10.1016/j.eswa.2007.08.016
  15. M. Lee, P. Choi, and Y. Woo, "A Hybrid Recommender System Combining Collaborative Filtering with Neural Network," Lecture Notes on Computer Sciences, Vol. 2347, pp. 531-534, 2002. DOI: 10.1007/3-540-47952-x_77
  16. L. Zhang, T. Qin, and P. Teng, "An Improved Collaborative Filtering Algorithm based on User Interest," Journal of Software, Vol. 9, No. 4, 2014. DOI: 10.4304/jsw.9.4.999-1006
  17. B. Zhu, R. Hurtado, J. Bobadilla, and F. Ortega, "An Efficient Recommender System Method based on the Numerical Relevances and the Non-numerical Structures of the Ratings," IEEE Access, Vol. 6, pp. 49935-49954, 2018. DOI: 10.1109/ACCESS.2018.2868464
  18. S. Lee, "Improving Performance of Jaccard Coefficient for Collaborative Filtering," Journal of The Korea Society of Computer and Information, Vol. 21, No. 11, pp. 121-126, 2016. DOI: 10.9708/jksci.2016.21.11.121