Abstract
As digitalization spreads across the globe, the amount of information available is increasing exponentially and users are suffering from information overload. Recommender systems present a feasible and effective means to guide and expose users to products and items which align with their preferences. Specifically with the boom of social networks, collaborative filtering recommender systems offer a means to suggest highly relevant items to a user based on their shared interests with other users in the system. Despite major advancements through the integration of machine learning and hybrid systems, collaborative filtering algorithms struggle to handle large and sparse datasets which hampers the system’s ability to provide accurate recommendations. Metaheuristic techniques have been successful in improving collaborative filtering recommender systems despite data size and sparsity. This study presents a review of different attempts to optimize collaborative filtering recommender systems inclusive of metaheuristic techniques in this evolution which highlights an evident gap in standardized evaluation metrics of recommender systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, 1 December 2000, pp. 241–250. https://doi.org/10.1145/358916.358995
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015). https://doi.org/10.1016/j.eij.2015.06.005
Roy, D., Dutta, M.: A systematic review and research perspective on recommender systems. J. Big Data 9(1), 59 (2022). https://doi.org/10.1186/s40537-022-00592-5
Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adap. Inter. 22, 317–355 (2012). https://doi.org/10.1007/s11257-011-9115-7
Kuo, R.J., Li, S.S.: Applying particle swarm optimization algorithm-based collaborative filtering recommender system considering rating and review. Appl. Soft Comput. 20, 110038 (2023). https://doi.org/10.1016/j.asoc.2023.110038
Jindal, H., Agarwal, S., Sardana, N.: PowKMeans: a hybrid approach for gray sheep users detection and their recommendations. Int. J. Inf. Technol. Web. Eng. 13(2), 56–69 (2018). https://doi.org/10.4018/IJITWE.2018040106
Ghazanfar, M.A., Prugel-Bennett, A.: Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst. Appl. 41(7), 3261–3275 (2014). https://doi.org/10.1016/j.eswa.2013.11.010
Chen, J., Zhao, C., Uliji, Chen, L.: Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering. Complex Intell. Syst. 6, 147–156 (2020). https://doi.org/10.1007/s40747-019-00123-5
Karabadji, N.E.I., Beldjoudi, S., Seridi, H., Aridhi, S., Dhifli, W.: Improving memory-based user collaborative filtering with evolutionary multi-objective optimization. Expert Syst. Appl. 98, 153–165 (2018). https://doi.org/10.1016/j.eswa.2018.01.015
Rashidi, R., Khamforoosh, K., Sheikhahmadi, A.: Proposing improved meta-heuristic algorithms for clustering and separating users in the recommender systems. Electron. Commer. Res. 1–26 (2022). https://doi.org/10.1007/s10660-021-09478-9
Alhijawi, B., Kilani, Y.: A collaborative filtering recommender system using genetic algorithm. Inf. Process. Manage. 57(6), 102310 (2020). https://doi.org/10.1016/j.ipm.2020.102310
Son, N.T., Ha, T.T.N., Jaafar, J.B., Anh, B.N., Giang, T.T.: Some metaheuristics for tourist trip design problem. In: 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA), pp. 1–10. IEEE, July 2023. https://doi.org/10.1109/ISIEA58478.2023.10212154
Alhijawi, B., Al-Naymat, G., Obeid, N., Awajan, A.: Novel predictive model to improve the accuracy of collaborative filtering recommender systems. Inf. Syst. 1(96), 101670 (2021). https://doi.org/10.1016/j.is.2020.101670
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994). https://doi.org/10.1145/192844
Tohidi, N., Dadkhah, C.: Improving the performance of video collaborative filtering recommender systems using optimization algorithm. Int. J. Nonlinear Anal. Appl. 11(1), 483–495 (2020). https://doi.org/10.22075/ijnaa.2020.19127.2058
Soltaninejad, F., Bidgoly, A.J.: A novel method for recommendation systems using invasive weed optimization (2021). arXiv preprint arXiv:2106.02831. https://doi.org/10.48550/arXiv.2106.02831
Katarya, R.: Movie recommender system with metaheuristic artificial bee. Neural Comput. Appl. 30(6), 1983–1990 (2018). https://doi.org/10.1007/s00521-017-3338-4
Yadav, S., Nagpal, S.: An improved collaborative filtering based recommender system using bat algorithm. Procedia Comput. Sci. 132, 1795–1803 (2018). https://doi.org/10.1016/j.procs.2018.05.155
Sharma, B., Hashmi, A., Gupta, C., Jain, A.: Collaborative recommender system based on improved firefly algorithm. Computación y Sistemas 26(2), 537–549 (2022). https://doi.org/10.13053/cys-26-2-4232
Kuo, R.J., Chen, C.K., Keng, S.H.: Application of hybrid metaheuristic with perturbation-based K-nearest neighbors algorithm and densest imputation to collaborative filtering in recommender systems. Inf. Sci. 575, 90–115 (2021). https://doi.org/10.1016/j.ins.2021.06.026
Pan, L., Shao, J.: Review of improved collaborative filtering recommendation algorithms. In: Yu, Z., Patnaik, S., Wang, J., Dey, N. (eds.) Advancements in Mechatronics and Intelligent Robotics. AISC, vol. 1220, pp. 21–26. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1843-7_3
Wu, W., He, L., Yang, J.: Evaluating recommender systems. In: Seventh International Conference on Digital Information Management (ICDIM 2012), pp. 56–61. IEEE, August 2012. https://doi.org/10.1109/ICDIM.2012.6360092
Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., Cosar, A.: Comput. Ind. Eng. 137, 106040 (2019). https://doi.org/10.1016/j.cie.2019.106040
Acknowledgments
I would like to sincerely thank Dr. Micheal Olusanya for his continuous support, guidance, and encouragement throughout this process. I would also like to thank the lecturers and members of the School of Computer Science and Applied Mathematics particularly Dr. Helen Robertson for her understanding, patience, and support during this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gebreselassie, M.H., Olusanya, M. (2024). The Use of Metaheuristics in the Evolution of Collaborative Filtering Recommender Systems: A Review. In: Sevaux, M., Olteanu, AL., Pardo, E.G., Sifaleras, A., Makboul, S. (eds) Metaheuristics. MIC 2024. Lecture Notes in Computer Science, vol 14754. Springer, Cham. https://doi.org/10.1007/978-3-031-62922-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-62922-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-62921-1
Online ISBN: 978-3-031-62922-8
eBook Packages: Computer ScienceComputer Science (R0)