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A new improved KNN-based recommender system

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Abstract

Despite all the developments in recommender systems and utilizations of successful application models in the industry, it can be said that there is still a need to improve various parts of these systems in order to enhance their effectiveness and scope of application. Recommender Systems (RS) are well-known in the field of e-commerce and are expected to provide customers with important goods and items (including music and movies). In traditional recommender systems suffer from important challenges and problems such as cold start, scalability, and data dispersion. Recently, some of these challenges have been successfully overcomed to an acceptable extent by the advantages of the combined use of these methods. The K-nearest neighbors (KNN)-based Recommender Systems (KRS) are among the most powerful recommender engines that are currently available. In these systems, the rating of a target item is predicted based on the average rating of similar items, where the similarity is defined based on a similarity measure and the average rating of items is treated as a feature. In this paper, KRS is developed by combining the following approaches: (a) using the mean and variance of the item ratings as item features to identify the similar items (i.e., item-based KRS or IKRS); (b) using the mean and variance of the user ratings as the user features to identify the similar users (i.e., user-based KRS or UKRS); (c) using weighted average to combine the neighboring user/item ratings; and (d) using ensemble learning. In this study, some methods, i.e., EVMRS (Ensemble Variance-Mean based Recommender System) and EWVMRS (Ensemble Weighted Variance-Mean based Recommender System) are discussed and an improved EWVMRSG (Ensemble Weighted Variance-Mean Based Recommender system enriched by Gaussian mixture model (GMM)) is expanded, which are all user-based and involve using Mean Distance as the measure of similarity between the users/items to find neighboring users/items, but then they use unweighted average, weighted average, and weighted averaging based on the full-covariance GMM, respectively, for prediction. Experimental evaluations show that the EVMRS, EWVMRS, and EWVMRSG methods, which all use ensemble learning, are the most accurate methods among those developed and evaluated in this study. Depending on the dataset, the EWVMRSG mothed achieves a 20–30% lower absolute error than that of the original Mean based Recommender Systems (MRS). In terms of execution time, the proposed method is comparable to the original MRS models and is much faster than the Slope-one, P-kNN, and C-kNN RSs.

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Availability of data and materials

The sample dataset is available in the Movielens data center.

References

  1. Logesh R, Subramaniyaswamy V, Malathi D, Sivaramakrishnan N, Vijayakumar VJNC (2020) Applications, Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput Appl 32(7):2141–2164

    Article  Google Scholar 

  2. Qin C et al (2020) ‘A survey on knowledge graph-based recommender systems.’ Sci Sin Inf 50(7):937–956

    Article  Google Scholar 

  3. Yadalam TV, Gowda VM, Kumar VS, Girish D, Namratha M (2020) Career recommendation systems using content based filtering In: 2020 5th International Conference on Communication and Electronics Systems (ICCES), 2020, pp. 660–665: IEEE

  4. Van Meteren R, Van Someren M (200) Using content-based filtering for recommendation. In: Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, Barcelona, 2000, pp. 47–56

  5. Ukey N, Yang Z, Li B, Zhang G, Hu Y, Zhang W (2023) Survey on exact kNN queries over high-dimensional data space. Sensors 23(2):629

    Article  Google Scholar 

  6. Ortega F, Lara-Cabrera R, Gonza’lez-Prieto A’, Bobadilla JJIS (2021) Providing reliability in recommender systems through Bernoulli matrix factorization. Inf Sci 553:110–128

    Article  MathSciNet  Google Scholar 

  7. Mohammadian M, Forghani Y, Torshiz MN (2021) An initialization method to improve the training time of matrix factorization algorithm for fast recommendation. Soft Comput 25(5):3975–3987

    Article  Google Scholar 

  8. Aggarwal CC (2016) Recommender systems. Springer

  9. Yue W, Wang Z, Liu W, Tian B, Lauria S, Liu XJN (2021) An optimally weighted user-and item-based collaborative filtering approach to predicting baseline data for Friedreich’s Ataxia patients. Neurocomputing 419:287–294

    Article  Google Scholar 

  10. Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Ricci F, Rokach L, Shapira B, Paul BK (eds) Recommender Systems Handbook. Springer, Boston, pp 107–144

    Chapter  Google Scholar 

  11. Ning X, Desrosiers C, Karypis G (2015) A comprehensive survey of neighborhood-based recommendation methods. In: Ricci F, Rokach L, Shapira B (eds) Recommender Systems Handbook. Springer, Boston, pp 37–76

    Chapter  Google Scholar 

  12. Jain G, Mahara T, Tripathi KN (2020) A survey of similarity measures for collaborative filtering-based recommender system. In: Pant M, Sharma KT, Verma OP, Singla R, Sikander A (eds) Soft Computing: Theories and Applications. Springer, Singapore, pp 343–352

    Chapter  Google Scholar 

  13. Liu H, Hu Z, Mian A, Tian H, Zhu XJKBS (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl Based Syst 56:156–166

    Article  Google Scholar 

  14. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Www 1:285–295

    Article  Google Scholar 

  15. Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci 178:37–51

    Article  Google Scholar 

  16. Park Y, Park S, Jung W, Lee S-G (2015) Reversed CF: a fast collaborative filtering algorithm using a k-nearest neighbor graph. Expert Syst Appl 42:4022–4028

    Article  Google Scholar 

  17. Wang J, De Vries AP and Reinders MJ (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 501–508

  18. Zheng M, Min F, Zhang H-R, Chen W-B (2016) Fast recommendations with the m-distance. IEEE Access 4:1464–1468

    Article  Google Scholar 

  19. Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM International Conference on Data Mining, 2005, pp. 471–475

  20. J. Li, L. Sun, and J. Wang, "A slope one collaborative filtering recommendation algorithm using uncertain neighbors optimizing," in International Conference on Web-Age Information Management, 2011, pp. 160–166.

  21. Wang Q-X, Luo X, Li Y, Shi X-Y, Gu L, Shang M-S (2018) Incremental Slope-one recommenders. Neurocomputing 272:606–618

    Article  Google Scholar 

  22. Bu J, Shen X, Xu B, Chen C, He X, Cai D (2016) Improving collaborative recommendation via user-item subgroups. IEEE Trans Knowl Data Eng 28:2363–2375

    Article  Google Scholar 

  23. Salter J, Antonopoulos N (2006) CinemaScreen recommender agent: combining collaborative and content-based filtering. IEEE Intell Syst 21:35–41

    Article  Google Scholar 

  24. Salter J, Antonopoulos N (2006) CinemaScreen recommender agent: combining collaborative and content-based filtering. IEEE Intell Syst 21(1):35–41

    Article  Google Scholar 

  25. Qi J, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl-Based Syst 91:143–151

    Article  Google Scholar 

  26. Yao Y (2015) Rough sets and three-way decisions. In: International Conference on Rough Sets and Knowledge Technology, pp. 62–73

  27. Yao Y (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180:341–353

    Article  MathSciNet  Google Scholar 

  28. Condli MK, Lewis DD, Madigan D, Posse C (1999) Bayesian mixed-E ects models for recommender systems. In: ACM SIGIR

  29. Yuan Y, Luo X, Shang M-S (2018) Effects of preprocessing and training biases in latent factor models for recommender systems. Neurocomputing 275:2019–2030

    Article  Google Scholar 

  30. Ren L, Wang W (2018) An SVM-based collaborative filtering approach for Top-N web services recommendation. Fut Gener Comput Syst 78:531–543

    Article  Google Scholar 

  31. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13:18–28

    Article  Google Scholar 

  32. Wu H, Zhang Z, Yue K, Zhang B, He J, Sun L (2018) Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl-Based Syst 145:46–58

    Article  Google Scholar 

  33. Wu D, Shang M, Luo X, Wang Z (2021) An L1-and-L2-norm-oriented latent factor model for recommender systems. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3071392

    Article  Google Scholar 

  34. Wu D, He Q, Luo X, Shang M, He Y, Wang G (2022) A posterior-neighborhood-regularized latent factor model for highly accurate web service QoS prediction. IEEE Trans Serv Comput 15(2):793–805

    Article  Google Scholar 

  35. Wu D, Luo X, Shang M, He Y, Wang G, Zhou M (2021) A deep latent factor model for high-dimensional and sparse matrices in recommender systems. IEEE Trans Syst Man Cybern: Syst 51(7):4285–4296

    Article  Google Scholar 

  36. Walek B, Fajmon P (2023) A hybrid recommender system for an online store using a fuzzy expert system. Exp Syst Appl 212:118565

    Article  Google Scholar 

  37. Chen Y, Liu Z, Li J, McAuley J, Xiong C (2022) Intent contrastive learning for sequential recommendation. arXiv preprint arXiv:2202.02519

  38. Wu F, Qiao Y, Chen J-H, et al (2020b) Mind: a large-scale dataset for news recommendation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp 3597–3606.

  39. Bagherifard K, Rahmani M, Nilashi M, Rafe V (2017) Performance improvement for recommender systems using ontology. Telemat Inf 34(8):1772–1792

    Article  Google Scholar 

  40. Bagherifard K, Rahmani M, Nilashi M, Rafe V, Nilashi M (2018) A recommendation method based on semantic similarity and complementarity using weighted taxonomy: a case on construction materials dataset. J Inf Knowl Manag 17(1):1–26

    Google Scholar 

  41. Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst Appl 92:507–520

    Article  Google Scholar 

  42. Wang H, Wang N (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1235–1244. https://doi.org/10.1145/2783258.2783273.

  43. Bathla G, Aggarwal H, Rani R (2020) Autotrustrec: recommender system with social trust and deep learning using autoencoder. Multim Tools Appl 79(29):20845–20860

    Article  Google Scholar 

  44. Li Y, Liu J, Ren J, Chang Y (2020) A novel implicit trust recommendation approach for rating prediction. IEEE Access 8:98305–98315

    Article  Google Scholar 

  45. Zhang C, Yu L, Wang Y, Shah C, Zhang X (2017) Collaborative user network embedding for social recommender systems. In: Proceedings of the 2017 SIAM international conference on data mining, SIAM, pp 381–389.

  46. Anwar T et al (2022) Collaborative filtering and kNN based recommendation to overcome cold start and sparsity issues: a comparative analysis. Multim Tools Appl 81(25):35693–35711

    Article  Google Scholar 

  47. Son LH (2016) Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf Syst 58:87–104

    Article  Google Scholar 

  48. Vanitha V, Krishnan P (2019) A modified ant colony algorithm for personalized learning path construction. J Intell Fuzzy Syst 37:6785–6800

    Article  Google Scholar 

  49. Silva N, Carvalho D, Pereira AC, Mourão F, Rocha L (2019) The pure cold-start problem: a deep study about how to conquer first-time users in recommendations domains. Inf Syst 80:1–12

    Article  Google Scholar 

  50. Almeida JR, Monteiro E, Silva LB, Sierra AP, Oliveira JL (2020) A recommender system to help discovering cohorts in rare diseases In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 25–28

  51. Zhong J, Xie H, Wang FL (2019) The research trends in recommender systems for e-learning: a systematic review of ssci journal articles from 2014 to 2018. Asian Association of Open Universities Journal

  52. Romero L, Saucedo C, Caliusco M, Gutiérrez M (2019) Supporting self-regulated learning and personalization using eportfolios: a semantic approach based on learning paths. Int J Educ Technol High Educ 16:1–16

    Article  Google Scholar 

  53. Koren Y (2008) Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining pp. 426–434

  54. Pirasteh P, Hwang D, Jung JJ (2016) Exploiting matrix factorization to asymmetric user similarities in recommendation systems. Knowl-Based Syst 83:51–57

    Article  Google Scholar 

  55. Ji K, Sun R, Li X, Shu W (2016) Improving matrix approximation for recommendation via a clustering-based reconstructive method. Neurocomputing 173:912–920

    Article  Google Scholar 

  56. Chen S, Peng Y (2018) Matrix factorization for recommendation with explicit and implicit feedback. Knowl-Based Syst 158:109–117

    Article  Google Scholar 

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Funding

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Payam Bahrani & Mitra Mirzarezaee

  2. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Behrouz Minaei-Bidgoli

  3. Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

  4. Department of Electrical Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, 75169, Iran

    Ahmad Keshavarz

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  1. Payam Bahrani
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  4. Mitra Mirzarezaee
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  5. Ahmad Keshavarz
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Contributions

PB designed the study; PB, BM, and HP wrote and edited the manuscript with the help from MM and AK. PB, BM, and HP carried out all the analyses, including the statistical analyses (with the help from MM and AK). PB generated all the figures and tables. All the authors have read and approved the final version of the paper.

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Correspondence to Hamid Parvin.

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Bahrani, P., Minaei-Bidgoli, B., Parvin, H. et al. A new improved KNN-based recommender system. J Supercomput 80, 800–834 (2024). https://doi.org/10.1007/s11227-023-05447-1

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