Comparing Recommender System Algorithms: From Traditional Collaborative Filtering to Deep Learning Autoencoder on MovieLens Datasets
DOI:
https://doi.org/10.52436/1.jutif.2026.7.4.5710Keywords:
Autoencoder, Collaborative Filtering, Content-Based Filtering, Hybrid Filtering, MovieLens, Performance Comparison, Recommender SystemsAbstract
With the increasing prevalence of digital platforms, recommender systems play a crucial role in personalizing user experiences. However, selecting an appropriate recommender system algorithm remains challenging due to inconsistent evaluation procedures and limited comparative studies that simultaneously address accuracy, efficiency, and architectural extensibility. This study compares nine recommender system algorithms, including traditional collaborative filtering (SVD, SVD++, KNN, NMF, SlopeOne), content-based filtering, hybrid approach, and deep learning autoencoder technique using MovieLens-1M and MovieLens-100K dataset. The hybrid approach achieved the best accuracy (RMSE 0.6865), followed by SVD++ and SVD (RMSE 0.8660 and 0.8729). Autoencoder demonstrated moderate performance (RMSE 0.9961) with 14% accuracy gap from SVD. Computational efficiency varied greatly, from 0.29 seconds for content-based methods to 3,470.73 seconds for SVD++. Cross-dataset validation of collaborative filtering methods confirmed consistency across datasets. Beyond accuracy metrics, architectural analysis identified autoencoder's extensibility advantage for integrating heterogeneous data sources, an important consideration for systems requiring multimodal capabilities. The findings provide empirical insights for method selection based on system priorities such as accuracy, efficiency, and architectural flexibility for multimodal integration.
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A. Sami, W. E. Adrousy, S. Sarhan, and S. Elmougy, “A deep learning based hybrid recommendation model for internet users,” Sci Rep, vol. 14, no. 1, p. 29390, Nov. 2024, doi: 10.1038/s41598-024-79011-z.
C. A. Gomez-Uribe and N. Hunt, “The Netflix Recommender System: Algorithms, Business Value, and Innovation,” ACM Trans. Manage. Inf. Syst., vol. 6, no. 4, pp. 1–19, Jan. 2016, doi: 10.1145/2843948.
E. Çano and M. Morisio, “Hybrid recommender systems: A systematic literature review,” IDA, vol. 21, no. 6, pp. 1487–1524, Nov. 2017, doi: 10.3233/IDA-163209.
S. Hong, J. Choi, Y.-C. Lee, S. Kumar, and N. Park, “SVD-AE: Simple Autoencoders for Collaborative Filtering,” in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, K. Larson, Ed., International Joint Conferences on Artificial Intelligence Organization, Aug. 2024, pp. 2054–2062. doi: 10.24963/ijcai.2024/227.
M. Elahi et al., “Towards responsible media recommendation,” AI Ethics, vol. 2, no. 1, pp. 103–114, Feb. 2022, doi: 10.1007/s43681-021-00107-7.
J. Son and S. B. Kim, “Content-based filtering for recommendation systems using multiattribute networks,” Expert Systems with Applications, vol. 89, pp. 404–412, Dec. 2017, doi: 10.1016/j.eswa.2017.08.008.
S. Sharma, V. Rana, and M. Malhotra, “Automatic recommendation system based on hybrid filtering algorithm,” Educ Inf Technol, vol. 27, no. 2, pp. 1523–1538, Mar. 2022, doi: 10.1007/s10639-021-10643-8.
D. Lian et al., “Scalable Content-Aware Collaborative Filtering for Location Recommendation,” IEEE Trans. Knowl. Data Eng., vol. 30, no. 6, pp. 1122–1135, June 2018, doi: 10.1109/TKDE.2018.2789445.
W. Zhang, P. Zhang, B. Zhang, X. Wang, and D. Wang, “A Collaborative Transfer Learning Framework for Cross-domain Recommendation,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach CA USA: ACM, Aug. 2023, pp. 5576–5585. doi: 10.1145/3580305.3599758.
Z. Movafegh and A. Rezapour, “Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition,” Engineering Applications of Artificial Intelligence, vol. 126, p. 107109, Nov. 2023, doi: 10.1016/j.engappai.2023.107109.
Z. Batmaz and C. Kaleli̇, “A new similarity-based multicriteria recommendation algorithm based onautoencoders,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 30, no. 3, pp. 855–870, Mar. 2022, doi: 10.55730/1300-0632.3815.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep Learning Based Recommender System: A Survey and New Perspectives,” ACM Comput. Surv., vol. 52, no. 1, pp. 1–38, Jan. 2020, doi: 10.1145/3285029.
S. Liang, Z. Pan, W. Liu, J. Yin, and M. De Rijke, “A Survey on Variational Autoencoders in Recommender Systems,” ACM Comput. Surv., vol. 56, no. 10, pp. 1–40, Oct. 2024, doi: 10.1145/3663364.
D. B. Rajesh and A. Kumar, “Collaborative filtering models an experimental and detailed comparative study,” Sci Rep, vol. 15, no. 1, p. 31667, Aug. 2025, doi: 10.1038/s41598-025-15096-4.
T. Anwar, V. Uma, and G. Srivastava, “Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++,” Int. J. Info. Tech. Dec. Mak., vol. 20, no. 04, pp. 1075–1093, July 2021, doi: 10.1142/S0219622021500310.
X. Guan, C.-T. Li, and Y. Guan, “Matrix Factorization With Rating Completion: An Enhanced SVD Model for Collaborative Filtering Recommender Systems,” IEEE Access, vol. 5, pp. 27668–27678, 2017, doi: 10.1109/ACCESS.2017.2772226.
G. Zhang, Y. Liu, and X. Jin, “A survey of autoencoder-based recommender systems,” Front. Comput. Sci., vol. 14, no. 2, pp. 430–450, Apr. 2020, doi: 10.1007/s11704-018-8052-6.
G. Varoquaux, P. R. Raamana, D. A. Engemann, A. Hoyos-Idrobo, Y. Schwartz, and B. Thirion, “Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines,” NeuroImage, vol. 145, pp. 166–179, Jan. 2017, doi: 10.1016/j.neuroimage.2016.10.038.
V. R. Joseph, “Optimal ratio for data splitting,” Statistical Analysis, vol. 15, no. 4, pp. 531–538, Aug. 2022, doi: 10.1002/sam.11583.
X. Wei, Z. Li, and J. Li, “Research on collaborative filtering recommendation algorithm with improved K-means clustering,” in Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), L. Shen and G. Zhong, Eds., Hangzhou, China: SPIE, Aug. 2023, p. 138. doi: 10.1117/12.2684486.
Z. You, H. Hu, Y. Wang, J. Xue, and X. Yi, “Improved Hybrid Collaborative Fitering Algorithm Based on Spark Platform,” Wuhan Univ. J. Nat. Sci., vol. 28, no. 5, pp. 451–460, Oct. 2023, doi: 10.1051/wujns/2023285451.
U. Kużelewska, “Effect of Dataset Size on Efficiency of Collaborative Filtering Recommender Systems with Multi-clustering as a Neighbourhood Identification Strategy,” in Computational Science – ICCS 2020, vol. 12139, V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, and J. Teixeira, Eds., in Lecture Notes in Computer Science, vol. 12139. , Cham: Springer International Publishing, 2020, pp. 342–354. doi: 10.1007/978-3-030-50420-5_25.
J. Bauer and D. Jannach, “Hybrid session-aware recommendation with feature-based models,” User Model User-Adap Inter, vol. 34, no. 3, pp. 691–728, July 2024, doi: 10.1007/s11257-023-09379-6.
M. Jallouli, S. Lajmi, and I. Amous, “When contextual information meets recommender systems: extended SVD++ models,” International Journal of Computers and Applications, vol. 44, no. 4, pp. 349–356, Apr. 2022, doi: 10.1080/1206212X.2020.1752971.
S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi, “Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data,” Expert Systems with Applications, vol. 149, p. 113248, July 2020, doi: 10.1016/j.eswa.2020.113248.
X. Wu, “Comparison Between Collaborative Filtering and Content-Based Filtering,” HSET, vol. 16, pp. 480–489, Nov. 2022, doi: 10.54097/hset.v16i.2627.
S. Dalmia et al., “LegoNN: Building Modular Encoder-Decoder Models,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 31, pp. 3112–3126, 2023, doi: 10.1109/TASLP.2023.3296019.
D. Ferreira, S. Silva, A. Abelha, and J. Machado, “Recommendation System Using Autoencoders,” Applied Sciences, vol. 10, no. 16, p. 5510, Aug. 2020, doi: 10.3390/app10165510.
T. Koike-Akino and Y. Wang, “Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction,” in 2020 IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, USA: IEEE, June 2020, pp. 2735–2740. doi: 10.1109/ISIT44484.2020.9174523.
C. Gao et al., “A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions,” ACM Trans. Recomm. Syst., vol. 1, no. 1, pp. 1–51, Mar. 2023, doi: 10.1145/3568022.
L. Wu et al., “A survey on large language models for recommendation,” World Wide Web, vol. 27, no. 5, p. 60, Sept. 2024, doi: 10.1007/s11280-024-01291-2.
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Copyright (c) 2026 Imam Fahrur Rozi, Triyanna Widiyaningtyas, Wahyu Caesarendra
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