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Enhancing the accuracy of collaborative filtering based recommender system with novel similarity measure

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Abstract

One of the most effective and extensively used recommendation technique is collaborative filtering. Based on related users or items, collaborative filtering creates recommendations for the users. Similarity measures play a crucial role in Collaborative Filtering Recommender System. One useful similarity measure for a cold-start situation is the Proximity-Impact-Popularity (PIP) measure. The PIP measure is, nevertheless, subject to several limitations as it penalises users’ multiple times throughout similarity computation in addition to ignoring their global rating behaviour. When a user rates the items, their overall rating behavior—whether they are lenient or strict—is referred to as global rating behaviour. In this study, we introduce an improved similarity metric to calculate similarity more accurately and generate high-quality recommendations. Our method takes into account both the user's rating behaviour and the percentage of co-rated items among users. Additionally, we have considered the computation complexity of the suggested work. In addition, to exhibit the performance of the proposed measure, empirical analysis has been done on real datasets. The results of the experiments performed on the dataset show that the suggested work takes precedence over the current similarity metrics. In comparison to state-of-the-art measurements, the suggested work exhibits improvements in MAE of 1.73%, RMSE of 4.01%, and F measure of 1.47% on an average.

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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 Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2005.99

    Article  Google Scholar 

  2. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2013.03.012

    Article  Google Scholar 

  3. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: An open architecture for collaborative filtering of netnews. https://doi.org/10.1145/192844.192905

  4. Bedi P, Kaur H (2006) Trust based personalized recommender system. INFOCOMP J Comput Sci 5(1):19–26. [Online]. Available: https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/118

  5. Shoham Y, Balabanović M (1997) Fab: Content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Article  Google Scholar 

  6. Ben Schafer J, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4321 LNCS, no. January, pp. 291–324. https://doi.org/10.1007/978-3-540-72079-9_9

  7. Çano E, Morisio M (2017) Hybrid recommender systems: A systematic literature review. Intell Data Anal 21(6):1487–1524. https://doi.org/10.3233/IDA-163209

    Article  Google Scholar 

  8. Paradarami TK, Bastian ND, Wightman JL (2017) A hybrid recommender system using artificial neural networks. Expert Syst Appl 83:300–313. https://doi.org/10.1016/j.eswa.2017.04.046

    Article  Google Scholar 

  9. Jeong B, Lee J, Cho H (2010) Improving memory-based collaborative filtering via similarity updating and prediction modulation. Inf Sci (Ny) 180(5):602–612. https://doi.org/10.1016/j.ins.2009.10.016

    Article  Google Scholar 

  10. Aggarwal CC (2016) Model-based collaborative filtering. In: Recommender Systems: The Textbook. Springer International Publishing, Cham, pp. 71–138. https://doi.org/10.1007/978-3-319-29659-3_3

  11. Chien YH, George EI, Chen Y, George EI (1999) A bayesian model for collaborative filtering. Direct

  12. Nasiri E, Berahmand K, Li Y (2023) Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks. Multimed Tools Appl 82(3):3745–3768. https://doi.org/10.1007/s11042-022-12943-8

    Article  Google Scholar 

  13. Yadav P, Tyagi S (2017) Hybrid fuzzy collaborative filtering: an integration of item-based and user-based clustering techniques. Int J Comput Sci Eng 15(3/4):295–310. https://doi.org/10.1504/IJCSE.2017.087413

    Article  Google Scholar 

  14. Pennock DM, Horvitz EJ, Lawrence S, Giles CL (2013) Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. arXiv preprint arXiv:1301.3885

  15. Forouzandeh S, Rostami M, Berahmand K (2022) A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and topsis model. Fuzzy Inf Eng 14(1):26–50. https://doi.org/10.1080/16168658.2021.2019430

    Article  Google Scholar 

  16. Lima GR, Mello CE, Lyra A, Zimbrao G (2020) Applying landmarks to enhance memory-based collaborative filtering. Inf Sci (Ny) 513:412–428. https://doi.org/10.1016/j.ins.2019.10.041

    Article  Google Scholar 

  17. Chang TM, Hsiao WF, Chang WL (2014) An ordinal regression model with SVD Hebbian learning for collaborative recommendation. J Inf Sci Eng. https://doi.org/10.6688/JISE.2014.30.2.7

    Article  Google Scholar 

  18. Stephen SC, Xie H, Rai S (2017) Measures of similarity in memory-based collaborative filtering recommender system - A comparison. ACM Int Conf Proceeding Ser Part F1296. https://doi.org/10.1145/3092090.3092105

  19. Jain G, Mahara T, Tripathi KN (2020) A survey of similarity measures for collaborative filtering-based recommender system. Adv Intell Syst Comput 1053(March):343–352. https://doi.org/10.1007/978-981-15-0751-9_32

    Article  Google Scholar 

  20. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24:513–523

    Article  Google Scholar 

  21. Jamali M, Ester M (2009) TrustWalker: A random walk model for combining trust-based and item-based recommendation. Proc ACM SIGKDD Int Conf Knowl Discov Data Min 397–405. https://doi.org/10.1145/1557019.1557067

  22. Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval, SIGIR, pp. 230–237. https://doi.org/10.1145/312624.312682

  23. Shardanand U, Maes P (1995) Social information filtering: Algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI conference on Human factors in computing systems. pp 210-217

  24. Bobadilla J, Serradilla F, Bernal J (2010) A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Syst 23(6):520–528. https://doi.org/10.1016/j.knosys.2010.03.009

    Article  Google Scholar 

  25. Liang S, Ma L, Yuan F (2015) A singularity-based user similarity measure for recommender systems. Int J Innov Comput Inf Control 11(5):1629–1638

    Google Scholar 

  26. Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci (Ny) 178(1):37–51. https://doi.org/10.1016/j.ins.2007.07.024

    Article  Google Scholar 

  27. 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 Syst 56:156–166. https://doi.org/10.1016/j.knosys.2013.11.006

    Article  Google Scholar 

  28. Manochandar S, Punniyamoorthy M (2021) A new user similarity measure in a new prediction model for collaborative filtering. Appl Intell 51(1):586–615. https://doi.org/10.1007/s10489-020-01811-3

    Article  Google Scholar 

  29. Patra BK, Launonen R, Ollikainen V, Nandi S (2015) A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowledge-Based Syst 82(March):163–177. https://doi.org/10.1016/j.knosys.2015.03.001

    Article  Google Scholar 

  30. Sun SB et al (2017) Integrating triangle and jaccard similarities for recommendation. PLoS ONE 12(8):1–16. https://doi.org/10.1371/journal.pone.0183570

    Article  Google Scholar 

  31. Iftikhar A, Ghazanfar MA, Ayub M, Mehmood Z, Maqsood M (2020) An improved product recommendation method for collaborative filtering. IEEE Access 8:123841–123857. https://doi.org/10.1109/ACCESS.2020.3005953

    Article  Google Scholar 

  32. Ayub M et al (2019) Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems. PLoS One 14(8). https://doi.org/10.1371/journal.pone.0220129

  33. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst. https://doi.org/10.1145/963770.963772

    Article  Google Scholar 

  34. Javed U, Shaukat K, Hameed IA, Iqbal F, Alam TM, Luo S (2021) A Review of content-based and context-based recommendation systems. Int J Emerg Technol Learn 16(3):274–306. https://doi.org/10.3991/ijet.v16i03.18851

    Article  Google Scholar 

  35. Forouzandeh S, Aghdam AR, Forouzandeh S, Xu S (2020) Addressing the cold-start problem using data mining techniques and improving recommender systems by cuckoo algorithm: a case study of facebook. Comput Sci Eng 22:62–73

    Article  Google Scholar 

  36. Ni J, Cai Y, Tang G, Xie Y (2021) Collaborative filtering recommendation algorithm based on TF-IDF and user characteristics. Appl Sci 11(20). https://doi.org/10.3390/app11209554

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Authors and Affiliations

  1. Shyama Prasad Mukherji College for Women, University of Delhi, Delhi, 110026, India

    Pratibha Yadav & Jaya Gera

  2. Hansraj College, University of Delhi, Delhi, 110007, India

    Harmeet Kaur

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  1. Pratibha Yadav
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  2. Jaya Gera
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  3. Harmeet Kaur
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Correspondence to Jaya Gera.

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Dataset

Used for this work is available at the following link: https://grouplens.org/datasets/movielens/ and https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm

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Yadav, P., Gera, J. & Kaur, H. Enhancing the accuracy of collaborative filtering based recommender system with novel similarity measure. Multimed Tools Appl 83, 47609–47626 (2024). https://doi.org/10.1007/s11042-023-17428-w

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  • DOI: https://doi.org/10.1007/s11042-023-17428-w

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