ICCK Transactions on Advanced Computing and Systems Home About Editors Current Issue
Performance Evaluation of Collaborative Filtering Recommender System on MovieLens Dataset
Research Article  ·  Published: 14 February 2026
Issue cover
ICCK Transactions on Advanced Computing and Systems
Volume 2, Issue 2, 2026: 137-157
Research Article Open Access

Performance Evaluation of Collaborative Filtering Recommender System on MovieLens Dataset

by
Muhammad Ahmad Hafeez Muhammad Ahmad Hafeez 1,†
Tahir Sher Tahir Sher 2,† ORCID
Abdul Rehman Abdul Rehman 3 * ORCID
Imran Ihsan Imran Ihsan 1 ORCID
1 Department of Creative Technologies, Air University, Islamabad 44000, Pakistan
2 Department of Artificial Intelligence, Korea University, Seoul 02842, Republic of Korea
3 Convergence Institute of Human Data Technology, Jeonju University, Jeonju 55069, Republic of Korea
† These authors contributed equally to this work
* Corresponding Author: Abdul Rehman, [email protected]
Volume 2, Issue 2
Volume 2, Issue 2 2026
Received: 18 July 2025 Revised: 10 August 2025 Accepted: 12 August 2025 Published: 14 February 2026 CrossMark
Download PDF 3.88 MB Cite Metrics
DOI: 10.62762/TACS.2025.714333
ARK: ark:/57805/tacs.2025.714333
You have full access to this open access article · CC BY 4.0 License

Article Information

Published in ICCK Transactions on Advanced Computing and Systems
Volume/Issue Volume 2, Issue 2, 2026
Pages 137-157

Abstract

In today's technological landscape, recommender systems provide essential personalized suggestions by leveraging user preferences. This study evaluates the computational performance of User-Based (UBCF) and Model-Based Collaborative Filtering (MBCF) as intelligent computing systems on the MovieLens 1M dataset, comparing performance on complete data versus partitions based on age and occupation. Using MAE and RMSE metrics with an 80/20 train-test split, we assessed UBCF with Euclidean/Cosine similarity and MBCF with NMF/SVD. Results show MBCF with SVD achieved the best performance (MAE: 0.6909, RMSE: 0.8761), outperforming UBCF by approximately 5.2% in MAE and 5.1% in RMSE (p $<$ 0.05). This confirms that SVD excels with complete datasets by effectively capturing latent factors, while demographic partitioning reduces accuracy due to data sparsity. From a computing systems perspective, UBCF suits real-time environments owing to lower computational overhead, whereas MBCF is better suited to offline batch-processing architectures in large-scale intelligent systems. Future work will explore hybrid models combining global and partition-based analysis with deep learning for enhanced personalization in next-generation computing architectures.

Graphical Abstract

Performance Evaluation of Collaborative Filtering Recommender System on MovieLens Dataset

Keywords

recommender system user-based & model-based collaborative filtering cosine similarity euclidean similarity non-negative matrix factorization singular value decomposition mean absolute error root mean squared error scalable computing systems

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

References

  1. Bhatnagar, V. (2016). Collaborative filtering using data mining and analysis. IGI Global.
    [Google Scholar]
  2. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
    [CrossRef] [Google Scholar]
  3. Francesco, R., Lior, R., & Bracha, S. (2015). Recommender systems: introduction and challenges. In Recommender systems handbook (pp. 1-34). Springer.
    [CrossRef] [Google Scholar]
  4. Yang, X., Guo, Y., Liu, Y., & Steck, H. (2014). A survey of collaborative filtering based social recommender systems. Computer Communications, 41, 1–10.
    [CrossRef] [Google Scholar]
  5. Lü, L., Medo, M., Yeung, C. H., Zhang, Y. C., Zhang, Z. K., & Zhou, T. (2012). Recommender systems. Physics Reports, 519(1), 1–49.
    [CrossRef] [Google Scholar]
  6. Koren, Y., Rendle, S., & Bell, R. (2021). Advances in collaborative filtering. Recommender systems handbook, 91-142.
    [CrossRef] [Google Scholar]
  7. Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets. Cambridge University Press.
    [CrossRef] [Google Scholar]
  8. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182).
    [CrossRef] [Google Scholar]
  9. Yuan, T., Cheng, J., Zhang, X., Qiu, S., & Lu, H. (2014, June). Recommendation by mining multiple user behaviors with group sparsity. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 28, No. 1).
    [CrossRef] [Google Scholar]
  10. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.
    [CrossRef] [Google Scholar]
  11. Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.
    [Google Scholar]
  12. Rendle, S. (2010, December). Factorization machines. In 2010 IEEE International conference on data mining (pp. 995-1000). IEEE.
    [CrossRef] [Google Scholar]
  13. Takács, G., Pilászy, I., Németh, B., & Tikk, D. (2008, October). Matrix factorization and neighbor based algorithms for the netflix prize problem. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 267-274).
    [CrossRef] [Google Scholar]
  14. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4), 331-370.
    [CrossRef] [Google Scholar]
  15. Bell, R. M., Koren, Y., & Volinsky, C. (2007). Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 95-104).
    [CrossRef] [Google Scholar]
  16. Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web: methods and strategies of web personalization (pp. 291-324). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef] [Google Scholar]
  17. Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009(1), 421425.
    [CrossRef] [Google Scholar]
  18. Lokesh, A. (2019). A Comparative Study of Recommendation Systems. Masters Theses & Specialist Projects. Paper 3166. https://digitalcommons.wku.edu/theses/3166/
    [Google Scholar]
  19. Desrosiers, C., & Karypis, G. (2010). A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook, 107-144.
    [CrossRef] [Google Scholar]
  20. Aggarwal, C. C., & Aggarwal, C. C. (2016). Model-based collaborative filtering. In Recommender Systems: The Textbook (pp. 71–138). Springer.
    [CrossRef] [Google Scholar]
  21. Luo, X., Zhou, M., Xia, Y., & Zhu, Q. (2014). An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics, 10(2), 1273–1284.
    [CrossRef] [Google Scholar]
  22. Xue, G. R., Lin, C., Yang, Q., Xi, W., Zeng, H. J., Yu, Y., & Chen, Z. (2005, August). Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 114-121).
    [CrossRef] [Google Scholar]
  23. Shardanand, U., & Maes, P. (1995, May). Social information filtering: Algorithms for automating “word of mouth”. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 210-217).
    [CrossRef] [Google Scholar]
  24. Black, P. E. (2006). Manhattan distance. In Dictionary of Algorithms and Data Structures. Retrieved from https://xlinux.nist.gov/dads/HTML/manhattanDistance.html (accessed on 31 December 2025).
    [Google Scholar]
  25. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295).
    [CrossRef] [Google Scholar]
  26. Cremonesi, P., Koren, Y., & Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems (pp. 39-46).
    [CrossRef] [Google Scholar]
  27. Takács, G., Pilászy, I., Németh, B., & Tikk, D. (2009). Scalable Collaborative Filtering Approaches for Large Recommender Systems. The Journal of Machine Learning Research, 10, 623-656.
    [Google Scholar]
  28. Paterek, A. (2007). Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD cup and workshop (Vol. 2007, pp. 5-8).
    [Google Scholar]
  29. Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434).
    [CrossRef] [Google Scholar]
  30. Gunawardana, A., Shani, G., & Yogev, S. (2012). Evaluating recommender systems. In Recommender systems handbook (pp. 547-601). New York, NY: Springer US.
    [CrossRef] [Google Scholar]
  31. Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.
    [CrossRef] [Google Scholar]
  32. Ahuja, R., Solanki, A., & Nayyar, A. (2019). Movie recommender system using k-means clustering and k-nearest neighbor. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 263–268). IEEE.
    [CrossRef] [Google Scholar]
  33. Katarya, R., & Verma, O. P. (2016). A collaborative recommender system enhanced with particle swarm optimization technique. Multimedia Tools and Applications, 75(15), 9225-9239.
    [CrossRef] [Google Scholar]
  34. Alam, M. T., Ubaid, S., Sohail, S. S., Nadeem, M., Hussain, S., Siddiqui, J., & others. (2021). Comparative analysis of machine learning based filtering techniques using MovieLens dataset. Procedia Computer Science, 194, 210–217.
    [CrossRef] [Google Scholar]
  35. Singh, P. K., Pramanik, P. K. D., & Choudhury, P. (2019). A comparative study of different similarity metrics in highly sparse rating dataset. In Data Management, Analytics and Innovation: Proceedings of ICDMAI 2018, Volume 2 (pp. 45–60). Springer.
    [CrossRef] [Google Scholar]
  36. Zhang, J., Wang, Y., Yuan, Z., & Jin, Q. (2019). Personalized real-time movie recommendation system: Practical prototype and evaluation. Tsinghua Science and Technology, 25(2), 180–191.
    [CrossRef] [Google Scholar]
  37. Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., & Jararweh, Y. (2018). A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In 2018 9th International Conference on Information and Communication Systems (ICICS) (pp. 102–106). IEEE.
    [CrossRef] [Google Scholar]
  38. Lund, J., & Ng, Y. K. (2018). Movie recommendations using the deep learning approach. In 2018 IEEE International Conference on Information Reuse and Integration (IRI) (pp. 47–54). IEEE.
    [CrossRef] [Google Scholar]
  39. Byström, H. (2013). Movie Recommendations from User Ratings. Stanford University. https://cs229.stanford.edu/proj2013/Bystrom-MovieRecommendationsFromUserRatings.pdf
    [Google Scholar]
  40. Gupta, G., & Katarya, R. (2019). Recommendation analysis on item-based and user-based collaborative filtering. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1–4). IEEE.
    [CrossRef] [Google Scholar]
  41. Guan, X., Li, C.-T., & Guan, Y. (2017). Matrix factorization with rating completion: An enhanced SVD model for collaborative filtering recommender systems. IEEE Access, 5, 27668–27678.
    [CrossRef] [Google Scholar]
  42. Ayyaz, S., & Qamar, U. (2017). Improving collaborative filtering by selecting an effective user neighborhood for recommender systems. In 2017 IEEE International Conference on Industrial Technology (ICIT) (pp. 1244–1249). IEEE.
    [CrossRef] [Google Scholar]
  43. Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web: methods and strategies of web personalization (pp. 325-341). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef] [Google Scholar]
  44. Sallam, R. M., Hussein, M., & Mousa, H. M. (2020). An enhanced collaborative filtering-based approach for recommender systems. International Journal of Computer Applications, 176(41), 9–15.
    [CrossRef] [Google Scholar]
  45. Kużelewska, U. (2014). Clustering algorithms in hybrid recommender system on movielens data. Studies in logic, grammar and rhetoric, 37(50), 125-139.
    [CrossRef] [Google Scholar]
  46. Hsu, S. H., Wen, M. H., Lin, H. C., Lee, C. C., & Lee, C. H. (2007, May). AIMED-A personalized TV recommendation system. In European conference on interactive television (pp. 166-174). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [CrossRef] [Google Scholar]
  47. Ma, T.-m., Wang, X., Zhou, F.-c., & Wang, S. (2023). Research on diversity and accuracy of the recommendation system based on multi-objective optimization. Neural Computing and Applications, 35(7), 5155–5163.
    [CrossRef] [Google Scholar]
  48. Pandya, S., Shah, J., Joshi, N., Ghayvat, H., Mukhopadhyay, S. C., & Yap, M. H. (2016, November). A novel hybrid based recommendation system based on clustering and association mining. In 2016 10th international conference on sensing technology (ICST) (pp. 1-6). IEEE.
    [CrossRef] [Google Scholar]
  49. Agrawal, S., & Jain, P. (2017). An improved approach for movie recommendation system. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 336–342). IEEE.
    [CrossRef] [Google Scholar]
  50. Ponnam, L. T., Punyasamudram, S. D., Nallagulla, S. N., & Yellamati, S. (2016). Movie recommender system using item based collaborative filtering technique. In 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) (pp. 1–5). IEEE.
    [CrossRef] [Google Scholar]
  51. Bobadilla, J., Hernando, A., Ortega, F., & Bernal, J. (2011). A framework for collaborative filtering recommender systems. Expert Systems with Applications, 38(12), 14609-14623.
    [CrossRef] [Google Scholar]
  52. Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015, May). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web (pp. 111-112).
    [CrossRef] [Google Scholar]
  53. Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining (pp. 263-272). IEEE.
    [CrossRef] [Google Scholar]
  54. Saraswat, M., Dubey, A., Naidu, S., Vashisht, R., & Singh, A. (2020). Web-Based Movie Recommender System. In Ambient Communications and Computer Systems: RACCCS 2019 (pp. 291–301). Springer.
    [CrossRef] [Google Scholar]
  55. Raghuwanshi, S. K., & Pateriya, R. K. (2021). Accelerated singular value decomposition (asvd) using momentum based gradient descent optimization. Journal of King Saud University-Computer and Information Sciences, 33(4), 447–452.
    [CrossRef] [Google Scholar]
  56. Harper, F. M., & Konstan, J. A. (2015). The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4), 1–19.
    [CrossRef] [Google Scholar]

Cite This Article

APA Style
Hafeez, M. A., Sher, T., Rehman, A., & Ihsan, I. (2026). Performance Evaluation of Collaborative Filtering Recommender System on MovieLens Dataset. ICCK Transactions on Advanced Computing and Systems, 2(2), 137-157. https://doi.org/10.62762/TACS.2025.714333
Export Citation
RIS Format
Compatible with EndNote, Zotero, Mendeley, and other reference managers
TY  - JOUR
AU  - Hafeez, Muhammad Ahmad
AU  - Sher, Tahir
AU  - Rehman, Abdul
AU  - Ihsan, Imran
PY  - 2026
DA  - 2026/02/14
TI  - Performance Evaluation of Collaborative Filtering Recommender System on MovieLens Dataset
JO  - ICCK Transactions on Advanced Computing and Systems
T2  - ICCK Transactions on Advanced Computing and Systems
JF  - ICCK Transactions on Advanced Computing and Systems
VL  - 2
IS  - 2
SP  - 137
EP  - 157
DO  - 10.62762/TACS.2025.714333
UR  - https://www.icck.org/article/abs/TACS.2025.714333
KW  - recommender system
KW  - user-based & model-based collaborative filtering
KW  - cosine similarity
KW  - euclidean similarity
KW  - non-negative matrix factorization
KW  - singular value decomposition
KW  - mean absolute error
KW  - root mean squared error
KW  - scalable computing systems
AB  - In today's technological landscape, recommender systems provide essential personalized suggestions by leveraging user preferences. This study evaluates the computational performance of User-Based (UBCF) and Model-Based Collaborative Filtering (MBCF) as intelligent computing systems on the MovieLens 1M dataset, comparing performance on complete data versus partitions based on age and occupation. Using MAE and RMSE metrics with an 80/20 train-test split, we assessed UBCF with Euclidean/Cosine similarity and MBCF with NMF/SVD. Results show MBCF with SVD achieved the best performance (MAE: 0.6909, RMSE: 0.8761), outperforming UBCF by approximately 5.2% in MAE and 5.1% in RMSE (p $
SN  - 3068-7969
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@article{Hafeez2026Performanc,
  author = {Muhammad Ahmad Hafeez and Tahir Sher and Abdul Rehman and Imran Ihsan},
  title = {Performance Evaluation of Collaborative Filtering Recommender System on MovieLens Dataset},
  journal = {ICCK Transactions on Advanced Computing and Systems},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {137-157},
  doi = {10.62762/TACS.2025.714333},
  url = {https://www.icck.org/article/abs/TACS.2025.714333},
  abstract = {In today's technological landscape, recommender systems provide essential personalized suggestions by leveraging user preferences. This study evaluates the computational performance of User-Based (UBCF) and Model-Based Collaborative Filtering (MBCF) as intelligent computing systems on the MovieLens 1M dataset, comparing performance on complete data versus partitions based on age and occupation. Using MAE and RMSE metrics with an 80/20 train-test split, we assessed UBCF with Euclidean/Cosine similarity and MBCF with NMF/SVD. Results show MBCF with SVD achieved the best performance (MAE: 0.6909, RMSE: 0.8761), outperforming UBCF by approximately 5.2\% in MAE and 5.1\% in RMSE (p \$},
  keywords = {recommender system, user-based \& model-based collaborative filtering, cosine similarity, euclidean similarity, non-negative matrix factorization, singular value decomposition, mean absolute error, root mean squared error, scalable computing systems},
  issn = {3068-7969},
  publisher = {Institute of Central Computation and Knowledge}
}

Article Metrics

Citations
Crossref
0
Scopus
0
Views
2694
PDF Downloads
371

Publisher's Note

ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and Permissions

CC BY Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ICCK Transactions on Advanced Computing and Systems
ICCK Transactions on Advanced Computing and Systems
ISSN: 3068-7969 (Online)
Portico
Preserved at
Portico

Article QR Code

Article QR Code
Scan to read this article
Submit Manuscript Edit a Special Issue

Popular Articles

Share This Article