A Comparative Study of Recommender Systems under Big Data Constraints
Article Sidebar
Main Article Content
Abstract
Abstract
Recommender Systems (RS) have become essential tools in a wide range of digital services, from e-commerce and streaming platforms to news and social media. As the volume of user-item interactions grows exponentially, especially in Big Data environments, selecting the most appropriate RS model becomes a critical task. This paper presents a
Comparative study of several state-of-the-art recommender algorithms, including EASE-R, SLIM, SLIM with ElasticNet regularization, Matrix Factorization (FunkSVD and ALS), P3Alpha, and RP3Beta. We evaluate these models according to key criteria such as scalability, computational complexity, predictive accuracy, and interpretability. The analysis considers both their theoretical underpinnings and practical applicability in large-scale scenarios. Our results highlight that while models like SLIM and SLIM-ElasticNet offer high accuracy and interpretability, they suffer from high computational costs, making them less suitable for real-time applications. In contrast, algorithms such as EASE-R and RP3Beta achieve a favorable balance between performance and scalability, proving more effective in large-scale environments. This study aims to provide guidelines for selecting the most appropriate recommender approach based on specific Big Data constraints and system requirements.
Downloads
Article Details
Copyright (c) 2016 Scrivano A.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. IEEE Comput. 2009;42(8):30–7. Available from: https://ieeexplore.ieee.org/document/5197422
Harper FM, Konstan JA. The MovieLens datasets: History and context. ACM Trans Interact Intell Syst. 2015;5(4):1–19. Available from: https://files.grouplens.org/papers/harper-tiis2015.pdf
He R, McAuley J. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web (WWW). 2016;507–17. Available from: https://dl.acm.org/doi/10.1145/2872427.2883037
Bennett J, Lanning S. The Netflix prize. In: Proceedings of KDD Cup and Workshop. 2007. Available from: https://www.cs.uic.edu/~liub/KDD-cup-2007/NetflixPrize-description.pdf
Zhou K, Yang S, Zha H. Functional matrix factorizations for cold-start recommendation. In: Proceedings of the 34th International ACM SIGIR Conference. 2012;315–24. Available from: https://www.researchgate.net/publication/221300242_Functional_Matrix_Factorizations_for_Cold-Start_Recommendation
Volkovs M, Yu GW, Poutanen T. DropoutNet: Addressing cold start in recommender systems. In: Advances in Neural Information Processing Systems (NeurIPS); 2017. Available from: https://papers.nips.cc/paper_files/paper/2017/hash/dbd22ba3bd0df8f385bdac3e9f8be207-Abstract.html
He X, Liao L, Zhang H, Nie L, Hu X, Chua T. Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web (WWW). 2017;173–82. Available from: https://dl.acm.org/doi/10.1145/3038912.3052569
Steck H. Embarrassingly shallow autoencoders for sparse data. In: Proceedings of the World Wide Web Conference (WWW). 2019;3251–7. Available from: https://dl.acm.org/doi/10.1145/3308558.3313710
Ning X, Karypis G. SLIM: Sparse linear methods for top-N recommender systems. In: 2011 IEEE 11th International Conference on Data Mining (ICDM). 2011;497–506. Available from: https://www.researchgate.net/publication/220765374_SLIM_Sparse_Linear_Methods_for_Top-N_Recommender_Systems
Cremonesi P, Koren Y, Turrin R. Performance of recommender algorithms on top-N recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys). 2010;39–46. Available from: https://dl.acm.org/doi/10.1145/1864708.1864721
He X, Deng K, Wang X, Li Y, Zhang Y, Wang M. LightGCN: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference. 2020;639–48. Available from: https://doi.org/10.1145/3397271.3401063
Dacrema MF, Cremonesi P, Jannach D. Are we making much progress? A worrying analysis of recent neural recommendation approaches. In: Proceedings of the 13th ACM Conference on Recommender Systems. 2019;101–9. Available from: https://dl.acm.org/doi/10.1145/3298689.3347058
Dacrema MF, Cremonesi P, Jannach D. A troubling analysis of reproducibility and progress in recommender systems research. ACM Trans Inf Syst. 2020;39(2):1–49. Available from: https://arxiv.org/abs/1911.07698