International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Using Fuzzy Rating Information for Collaborative Filtering-based Recommender Systems

  • Lee, Soojung (Department of Computer Education, Gyeongin National University of Education)
  • Received : 2020.06.16
  • Accepted : 2020.06.27
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

Keywords

References

  1. 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: https://doi.org/10.1109/ACCESS.2018.2883742.
  2. S. Bag, A. Ghadge, and M.K. Tiwari, "An Integrated Recommender System for Improved Accuracy and Aggregate Diversity", Computers and Industrial Engineering, Vol. 130, pp. 187-197, 2019. DOI: https://doi.org/10.1016/j.cie.2019.02.028.
  3. Y. Wu, X. Zhang, H. Yu, S. Wei, and W. Guo, "Collaborative Filtering Recommendation Algorithm based on User Fuzzy Similarity," Intelligent Data Analysis, Vol. 21, pp. 311-327, 2017. DOI: https://doi.org/10.3233/IDA-150438.
  4. K.G. Saranya, G.S. Sadasivam, and M. Chandralekha, “Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique,” Indian Journal of Science and Technology, Vol. 9, No. 29, pp. 1-8, Aug. 2016. DOI: https://10.17485/ijst/2016/v9i29/91060.
  5. J.L. Herlocker, J.A. Konstan, A. Borchers, and J. Riedl, "An Algorithmic Framework for Performing Collaborative Filtering," The Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230-237, 1999. DOI: http://doi.org/10.1145/312624.312682.
  6. H.-J. Kwon, T.-H. Lee, J.-H. Kim, and K.-S. Hong, "Improving Prediction Accuracy using Entropy Weighting in Collaborative Filtering," In 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, pp. 40-45, 2009. DOI: https://doi.org/10.1109/UIC-ATC.2009.50.
  7. J. Bobadilla, F. Serradilla, and J. Bernal, “A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems,” Knowledge-Based Systems, Vol. 23, No. 6, pp. 520-528, 2010. DOI: https://doi.org/10.1016/j.knosys.2010.03.009.
  8. S. Lee, "Improving Jaccard Index using Genetic Algorithms for Collaborative Filtering," Lecture Notes in Computer Science, Vol. 10385, pp. 378-385, 2017. DOI: https://doi.org/10.1007/978-3-319-61824-1_41.
  9. L.H. Son, "HU-FCF: A Hybrid User-based Fuzzy Collaborative Filtering Method in Recommender Systems," Expert Systems with Applications, Vol. 41, pp. 6861-6870, 2014. DOI: https://doi.org/10.1016/j.eswa.2014.05.001.
  10. M.Y.H. Al-Shamri and N.H. Al-Ashwal, "Fuzzy-weighted Similarity Measures for Memory-based Collaborative Recommender Systems," Journal of Intelligent Learning Systems and Applications, Vol. 6, pp. 1-10, 2014. DOI: https://doi.org/10.4236/jilsa.2014.61001.
  11. E.S.G. Herrera-Viedma, J.A. Olivas, A. Cerezo, and F.P. Romero, “A Google Wave-based Fuzzy Recommender System to Disseminate Information in University Digital Libraries 2.0,” Information Sciences, Vol. 181, No. 9, pp. 1503-1516, 2011. DOI: https://doi.org/10.1016/j.ins.2011.01.012.
  12. C. Desrosiers and G. Karypis, "A Novel Approach to Compute Similarities and Its Application to Item Recommendation," In PRICAI 2010: Trends in Artificial Intelligence, Lecture Notes in Computer Science, Vol. 6230/2010, pp. 39-51, 2010. DOI: https://doi.org/10.1007/978-3-642-15246-7_7.
  13. J. Bobadilla, F. Ortega, and A. Hernando, “A Collaborative Filtering Similarity Measure based on Singularities,” Information Processing and Management, Vol. 48, No. 2, pp. 204-217, 2012. DOI: https://doi.org/10.1016/j.ipm.2011.03.007.
  14. 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: https://doi.org/10.1109/ACCESS.2018.2868464.
  15. C.H. Piao, J. Zhao, and L.J. Zheng, “Research on Entropy-based Collaborative Filtering Algorithm and Personalized Recommendation in Ecommerce,” Service Oriented Computing and Applications, Vol. 3, No. 2, pp. 147-157, 2009. DOI: https://doi.org/10.1007/s11761-008-0034-3.
  16. W. Wang, G. Zhang, and J. Lu, “Collaborative Filtering with Entropy Driven User Similarity in Recommender Systems,” International Journal of Intelligent Systems, Vol. 30, No. 8, pp. 854-870, Aug. 2015. DOI: https://doi.org/10.1002/int.21735.