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An efficient approach for improving the predictive accuracy of multi-criteria recommender system

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Abstract

Recommender Systems are useful information filtering tools that have reduced information overload over the web. Collaborative filtering (CF) is one of the extensively used recommendation techniques. Traditional CF captures user-item ratings in a two-dimensional rating matrix which does not sufficiently convey user preferences. Ratings based on several criteria are incorporated into CF to develop multi-criteria recommender systems (MCRS). MCRS are more efficient and cater to the users’ needs with more satisfaction. However, there are certain issues like multidimensionality, sparsity, and cold start associated with MCRS. This paper aims to study the MCRS and investigate efficient solutions for existing issues. In this direction, we proposed a modified similarity measure that improves the accuracy of neighborhood generation and rating prediction. In the proposed approach, the users are clustered based on multi-criteria ratings, which reduces the data sparsity and multidimensionality issues in MCRS. The supremacy of the proposed approach is verified by conducting experiments on a benchmark data set and evaluating the performance using some standard evaluation measures.

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References

  1. Yannam VR, Kumar J, Babu KS, Sahoo B (2023) Improving group recommendation using deep collaborative filtering approach. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01205-x

    Article  Google Scholar 

  2. Anwar K, Zafar A, Iqbal A (2023) Neutrosophic MCDM approach for performance evaluation and recommendation of best players in sports league. Int J Neutrosophic Sci 20(01):128–149

    Article  Google Scholar 

  3. Anwar K, Siddiqui J, Sohail SS (2020) Machine learning-based book recommender system: a survey and new perspectives. Int J Intell Inf Database Syst 13(2–4):231–248. https://doi.org/10.1504/IJIIDS.2020.109457

    Article  Google Scholar 

  4. Sohail SS, Siddiqui J, Ali R (2019) A comprehensive approach for the evaluation of recommender systems using implicit feedback. Int J Inf Technol 11(3):549–567. https://doi.org/10.1007/s41870-018-0202-4

    Article  Google Scholar 

  5. Nadeem M et al (2022) Performance comparison of randomized and non-randomized learning algorithms based recommender systems. Int J Next-Generation Comput. https://doi.org/10.47164/ijngc.v13i3.820

    Article  Google Scholar 

  6. Anwar K, Siddiqui J, Saquib Sohail S (2019) Machine learning techniques for book recommendation: an overview. SSRN Electron J. https://doi.org/10.2139/ssrn.3356349

    Article  Google Scholar 

  7. Breese JS, Heckerman D, and Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: UAI’98: proceedings of the fourteenth conference on uncertainty in artificial intelligence. pp. 43–52, [Online]. Available: http://arxiv.org/abs/1301.7363.

  8. Jena KK et al (2022) Neural model based collaborative filtering for movie recommendation system. Int J Inf Technol 14(4):2067–2077. https://doi.org/10.1007/s41870-022-00858-4

    Article  Google Scholar 

  9. Wasid M, Ali R (2021) A frequency count approach to multi-criteria recommender system based on criteria weighting using particle swarm optimization. Appl Soft Comput 112:107782. https://doi.org/10.1016/j.asoc.2021.107782

    Article  Google Scholar 

  10. Gupta S, Kant V (2020) Credibility score based multi-criteria recommender system. Knowledge-Based Syst 196:105756. https://doi.org/10.1016/j.knosys.2020.105756

    Article  Google Scholar 

  11. Wasid M, Ali R (2019) Fuzzy side iinformation clustering-based framework for effective recommendations. Comput Inform 38:597–620. https://doi.org/10.31577/cai

    Article  Google Scholar 

  12. 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 

  13. Koutrika G, Bercovitz B, and Garcia-Molina H (2009) FlexRecs: expressing and combining flexible recommendations

  14. Mann SK, Chawla S (2023) A proposed hybrid clustering algorithm using K-means and BIRCH for cluster based cab recommender system (CBCRS). Int J Inf Technol 15(1):219–227. https://doi.org/10.1007/s41870-022-01113-6

    Article  Google Scholar 

  15. Wasid M, Ali R, Shahab S (2023) Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems. Heliyon 9(7):e18183. https://doi.org/10.1016/j.heliyon.2023.e18183

    Article  PubMed  PubMed Central  Google Scholar 

  16. Adomavicius G, Manouselis N, Kwon Y (2011) Multi-criteria recommender systems. Recommender systems handbook. Springer, Boston, pp 769–803. https://doi.org/10.1007/978-0-387-85820-3_24

    Chapter  Google Scholar 

  17. Nilashi M, Bin Ibrahim O, Norafida Ithnin RZ (2014) A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Comput 7(1):55–64. https://doi.org/10.1007/s00500-014-1475-

    Article  Google Scholar 

  18. Nilashi M, Jannach D, Bin Ibrahim O, Ithnin N (2015) Clustering- and regression-based multi-criteria collaborative filtering with incremental updates. Inf Sci (Ny) 293:235–250. https://doi.org/10.1016/j.ins.2014.09.012

    Article  Google Scholar 

  19. Kermany NR, Alizadeh SH (2017) A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. Electron Commer Res Appl 21:50–64. https://doi.org/10.1016/j.elerap.2016.12.005

    Article  Google Scholar 

  20. Kant V, Jhalani T, Dwivedi P (2018) Enhanced multi-criteria recommender system based on fuzzy Bayesian approach. Multimed Tools Appl 77(10):12935–12953. https://doi.org/10.1007/s11042-017-4924-2

    Article  Google Scholar 

  21. Fan G, Zhang C, Chen J, Li P, Li Y, Leung VCM (2023) Improving rating prediction in multi-criteria recommender systems via a collective factor model. IEEE Trans Netw Sci Eng 14(8):1–11. https://doi.org/10.1109/TNSE.2023.3270910

    Article  Google Scholar 

  22. Wasid M, Ali R (2020) Multi-criteria clustering-based recommendation using Mahalanobis distance. Int J Reason Intell Syst 12(2):96–105. https://doi.org/10.1504/IJRIS.2020.106803

    Article  Google Scholar 

  23. Wasid M, Ali R (2018) An improved recommender system based on multi-criteria clustering approach. Procedia Comput Sci 131:93–101. https://doi.org/10.1016/j.procs.2018.04.190

    Article  Google Scholar 

  24. Zhang K, Liu X, Wang W, Li J (2021) Multi-criteria recommender system based on social relationships and criteria preferences. Expert Syst Appl 176:114868. https://doi.org/10.1016/j.eswa.2021.114868

    Article  Google Scholar 

  25. Mikeli A, Sotiros D, Apostolou D, Despotis D (2013) A multi-criteria recommender system incorporating intensity of preferences. IISA 2013- 4th Int Conf Information, Intell Syst Appl,. https://doi.org/10.1109/IISA.2013.6623719

    Article  Google Scholar 

  26. Nilashi M, Bin Ibrahim O, Ithnin N (2014) Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Syst 60:82–101. https://doi.org/10.1016/j.knosys.2014.01.006

    Article  Google Scholar 

  27. Nilashi M, Bin Ibrahim O, Ithnin N, Sarmin NH (2015) A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (EM) and PCA-ANFIS. Electron Commer Res Appl 14(6):542–562. https://doi.org/10.1016/j.elerap.2015.08.004

    Article  Google Scholar 

  28. Saqib M, Sahu SA, Sakib M, Al-Ammar EA (2021) Machine learning-based day-ahead market energy usage bidding for smart microgrids. Electr Veh Integr a Smart Microgrid Environ. https://doi.org/10.1201/9780367423926-10

    Article  Google Scholar 

  29. Anwar K et al (2023) Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance. Fractals. https://doi.org/10.1142/S0218348X23401497

    Article  Google Scholar 

  30. Wasid M, Ali R (2019) Clustering approach for multidimensional recommender systems. IEEE Int Conf Data Min Work ICDMW 2018-Novem:1122–1127. https://doi.org/10.1109/ICDMW.2018.00161

    Article  Google Scholar 

  31. APS Mohd Sakib, M Usama (2022) Bitcoin price prediction using machine learning. J Comput Sci Softw Dev. 2(1)

  32. M. Wasid and K. Anwar (2020) An augmented similarity approach for improving collaborative filtering based recommender system,” In 2022 International conference on data analytics for business and industry (ICDABI), 2022, pp. 751–755, doi: https://doi.org/10.1109/ICDABI56818.2022.10041638

  33. Alhijawi B, Fraihat S, Awajan A (2023) Multi-factor ranking method for trading-off accuracy, diversity, novelty, and coverage of recommender systems. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01158-1

    Article  Google Scholar 

  34. Lakiotaki K, Matsatsinis NF, Tsoukiàs A (2011) Multicriteria user modeling in recommender systems. IEEE Intell Syst 26(2):64–76. https://doi.org/10.1109/MIS.2011.33

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Aligarh Muslim University, Aligarh, 202002, India

    Khalid Anwar & Aasim Zafar

  2. K.A. Nizami Center for Quranic Studies, Aligarh Muslim University, Aligarh, 202002, India

    Arshad Iqbal

  3. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

    Khalid Anwar

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  1. Khalid Anwar
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  2. Aasim Zafar
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  3. Arshad Iqbal
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Correspondence to Khalid Anwar.

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Anwar, K., Zafar, A. & Iqbal, A. An efficient approach for improving the predictive accuracy of multi-criteria recommender system. Int. j. inf. tecnol. 16, 809–816 (2024). https://doi.org/10.1007/s41870-023-01547-6

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