Abstract
Recommender systems have emerged as effective solutions for managing information overload by providing personalized predictions. Nowadays a deep learning-based recommendation system has achieved remarkable outcomes, but the majority of these systems are single criteria recommenders that rely on a single rating. Multi-criteria recommender systems have recently become more popular as they provide better recommendations than single criteria. In this paper, our proposed work combines the power of deep matrix factorization and regression techniques for recommendation on multi-criteria ratings. First we have used deep matrix factorization to capture latent factors in user-item interactions on multi-criteria ratings. Secondly the recommendations generated by this model are aggregated using lasso and linear regression techniques to improve the overall recommendation accuracy. Experimental analysis is conducted on two datasets TripAdvisor and Yahoo!movie demonstrated that our proposed recommender system outperforms as compared to traditional recommendation models in terms of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Code Availability
Code will be made available on request.
Data availability
Data will be made available on reasonable request.
References
Kumar P, Thakur RS (2018) Recommendation system techniques and related issues: a survey. Int J Inform Technol 10:495–501
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 17(6):734–749
Anwar K, Zafar A, Iqbal A (2023) An efficient approach for improving the predictive accuracy of multi-criteria recommender system. Int J Inform Technol. https://doi.org/10.1007/s41870-023-01547-6
Al-Ghuribi SM, Noah SAM (2019) Multi-criteria review-based recommender system-the state of the art. IEEE Access 7:169446–169468
Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38
Fu M, Qu H, Yi Z, Lu L, Liu Y (2018) A novel deep learning-based collaborative filtering model for recommendation system. IEEE Trans Cybern 49(3):1084–1096
Jena KK, Bhoi SK, Mallick C, Jena SR, Kumar R, Long HV, Son NTK (2022) Neural model based collaborative filtering for movie recommendation system. Int J Inform Technol 14(4):2067–2077
Behera G, Nain N (2022) Deepnnmf: deep nonlinear non-negative matrix factorization to address sparsity problem of collaborative recommender system. Int J Inform Technol 14(7):3637–3645
Nassar N, Jafar A, Rahhal Y (2020) Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization. J Big Data 7(1):1–12
Sinha BB, Dhanalakshmi R (2022) Dnn-mf: deep neural network matrix factorization approach for filtering information in multi-criteria recommender systems. Neural Comput Appl 34(13):10807–10821
Choudhury SS, Mohanty SN, Jagadev AK (2021) Multimodal trust based recommender system with machine learning approaches for movie recommendation. Int J Inform Technol 13:475–482
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182
Nilashi M, Dalvi-Esfahani M, Roudbaraki MZ, Ramayah T, Ibrahim O (2016) A multi-criteria collaborative filtering recommender system using clustering and regression techniques. J Soft Comput Decis Support Syst 3(5)
Wu Y, Du Bois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 153–162
Ouyang Y, Liu W, Rong W, **ong Z (2014) Autoencoder-based collaborative filtering. In: Neural Information Processing: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part III 21, pp. 284–291. Springer
Georgiev K, Nakov P (2013) A non-iid framework for collaborative filtering with restricted boltzmann machines. In: International Conference on Machine Learning, pp. 1148–1156, PMLR
Salakhutdinov R, Mnih A, Hinton G (2007) Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798
Lakiotaki K, Matsatsinis NF, Tsoukias A (2011) Multicriteria user modeling in recommender systems. IEEE Intell Syst 26(2):64–76
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethics approval
Not Applicable.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Singh, R., Dwivedi, P. & Patidar, P. Multi-criteria recommendation system based on deep matrix factorization and regression techniques. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01780-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41870-024-01780-7