인터넷정보학회논문지 (Journal of Internet Computing and Services)

  • 제25권5호
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  • Pages.87-94
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  • 2024
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  • 1598-0170(pISSN)
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  • 2287-1136(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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협력 필터링에서 유전자 알고리즘을 활용한 최적 유사도 산출

Optimal Similarity Calculation using Genetic Algorithms in Collaborative Filtering

  • Soojung Lee (Dept. of Computer Education, Gyeongin National University of Education)
  • 투고 : 2024.07.08
  • 심사 : 2024.10.04
  • 발행 : 2024.10.31
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초록

협력 필터링 기반의 추천 시스템은 현 사용자를 위한 추천 항목들을 제공함에 있어서 유사한 인접 이웃들이 선호한 항목들을 우선적으로 고려하는 방식이다. 시스템의 성능을 위하여 유사도 척도는 매우 중요한데, 본 연구에서는 유전자 알고리즘을 활용하여 최적 성능을 가져오는 사용자 간 유사도 값을 산출하였으며, 특히 평가 항목 특성별로 유전자 알고리즘을 별도 실행하여 예측 정확도 성능을 높이고자 하였다. 성능 실험을 통하여 유전자 알고리즘의 연산 확률 적정값을 구하였고, 두 종류의 공개 데이터셋을 활용한 실험 결과로서 제안 방법의 예측 성능이 기존 방법들보다 우수하고, 특히 희소 데이터 환경에서 더욱 우수함을 확인하였다. 본 연구 결과는 개인화된 추천의 정확성을 개선하고, 대규모 사용자 및 항목 데이터가 존재하는 실세계 애플리케이션에서 유용하게 활용될 수 있다.

A collaborative filtering-based recommender system is a method that gives priority to items preferred by similar neighbors when providing recommended items for the current user. The similarity measure is very important for the performance of the system. In this study, a genetic algorithm was used to calculate the similarity value between users that results in optimal performance. In particular, the genetic algorithm was run separately for each rated item feature to improve prediction accuracy performance. Through performance experiments, the optimal probabilities of the genetic algorithm operators were obtained, and as a result of experiments using two types of public datasets, it was confirmed that the prediction performance of the proposed method was superior to that of existing methods, especially in a sparse data environment. The results of this study can improve the accuracy of personalized recommendations and be effectively applied in real-world applications with large-scale user and item data.

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참고문헌

  1. B. Shao, X. Li, and G. Bian, "A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph," Expert Systems with Applications, vol. 165, 2021. https://doi.org/10.1016/j.eswa.2020.113764
  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. https://doi.org/10.1109/ACCESS.2018.2883742
  3. F. Fkih, "Similarity measures for collaborative filtering-based recommender systems: review and experimental comparison," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 9, pp. 7645-7669, 2022. https://doi.org/10.1016/j.jksuci.2021.09.014
  4. H. Khojamli and J. Razmara, "Survey of similarity functions on neighborhood-based collaborative filtering," Expert Systems with Applications, vol. 185, 2021. https://doi.org/10.1016/j.eswa.2021.115482
  5. H. Zhou, F. Xiong, and H. Chen, "A comprehensive survey of recommender systems based on deep learning," Applied Sciences, vol. 13, no. 20: 11378, 2023. https://doi.org/10.3390/app132011378
  6. S. Bag, S.K. Kumar, and M.K. Tiwari, "An efficient recommendation generation using relevant Jaccard similarity," Information Sciences, vol. 483, pp. 53-64, 2019. https://doi.org/10.1016/j.ins.2019.01.023
  7. A. Iftikhar, M. A. Ghazanfar, M. Ayub, Z. Mehmood and M. Maqsood, "An improved product recommendation method for collaborative filtering," IEEE Access, vol. 8, pp. 123841-123857, 2020. https://doi.org/10.1109/ACCESS.2020.3005953
  8. A. Jain, P.K. Singh, and J. Dhar, "Multi-objective item evaluation for diverse as well as novel item recommendations," Expert Systems with Applications, vol. 139, 2020. https://doi.org/10.1016/j.eswa.2019.112857
  9. D. Wang, Y. Yih, and M. Ventresca, "Improving neighbor-based collaborative filtering by using a hybrid similarity measurement," Expert Systems with Applications, vol. 160, 2020. https://doi.org/10.1016/j.eswa.2020.113651
  10. B. Alhijawi and Y. Kilani, "A collaborative filtering recommender system using genetic algorithm," Information Processing & Management, vol. 57, no. 6, 2020. https://doi.org/10.1016/j.ipm.2020.102310
  11. F. Fkih, "Enhancing item-based collaborative filtering by users' similarities injection and low-quality data handling," Data & Knowledge Engineering, vol.144, 2023. https://doi.org/10.1016/j.datak.2022.102126
  12. H.I. Abdalla, Y.A. Amer, L. Nguyen, A.A. Amer, B.M. Al-Maqaleh, "Numerical similarity measures versus Jaccard for collaborative filtering," Proceedings of the 9th Int'l Conf. Advanced Intelligent Systems and Informatics, 2023. https://doi.org/10.1007/978-3-031-43247-7_20
  13. A. Gazdar and L. Hidri, "A new similarity measure for collaborative filtering based recommender systems," Knowledge-Based Systems, vol. 188, 2020. https://doi.org/10.1016/j.knosys.2019.105058
  14. Y. Mu, N. Xiao, R. Tang, L. Luo, and X. Yin, "An efficient similarity measure for collaborative filtering," Procedia Computer Science, vol. 147, pp. 416-421, 2019. https://doi.org/10.1016/j.procs.2019.01.258
  15. B. Alhijawi and Y. Kilani, "Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems," IEEE/ACIS 15th Int'l Conf. on Computer and Information Science, 2016. https://doi.org/10.1109/ICIS.2016.7550751
  16. Y. Ar and E. Bostanci, "A genetic algorithm solution to the collaborative filtering problem," Expert Systems with Applications, vol. 61, pp. 122-128, 2016. https://doi.org/10.1016/j.eswa.2016.05.021
  17. A. Laishram and V. Padmanabhan, "Discovery of user-item subgroups via genetic algorithm for effective prediction of ratings in collaborative filtering," Applied Intelligence, vol. 49, pp. 3990- 4006, 2019. https://doi.org/10.1007/s10489-019-01495-4
  18. Z. Liu, L. Wang, X. Li, and S. Pang, "A multi-attribute personalized recommendation method for manufacturing service composition with combining collaborative filtering and genetic algorithm," J. of Manufacturing Systems, vol. 58, pp. 348-364, 2021. https://doi.org/10.1016/j.jmsy.2020.12.019
  19. F.H. Nanehkaran, S.M. Lajevardi, and M.M. Bidgholi, "Optimization of fuzzy similarity by genetic algorithm in user-based collaborative filtering recommender systems," Expert Systems, vol. 39, no. 4, 2022. https://doi.org/10.1111/exsy.12893
  20. B.S. Neysiani, N. Soltani, R. Mofidi, and M.H. Nadimi-Shahraki, "Improving performance of association rule-based collaborative filtering recommendation systems using genetic algorithm," Int'l J. of Information Technology and Computer Science, vol. 11, no. 2, 2019. https://doi.org/10.5815/ijitcs.2019.02.06
  21. M. Salehi, I.N. Kamalabadi, and M.B. GhaznaviGhoushchi, "Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm," International Journal of Business Information Systems, vol. 13, no. 3, pp. 265-283, 2013. https://doi.org/10.1504/IJBIS.2013.054465