Skip to main content
SpringerLink
Log in

Improving the Data Quality of the MovieLens Dataset Using Dimensionality Reduction Techniques

  • Conference paper
  • First Online:
Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

  • 3233 Accesses

Abstract

The presence of recommendation systems within the current era is not controlled or even managed like before, for providing the optimum services to users. For that reason, these services need to be in an ideal and complete state since the real-world data is often not that much perfect. That optimum state could not be achieved without the interaction of preprocessing method. Preprocessing can offer a lot of insights about the data and its structure and quality. Preprocessing techniques ensure that the dataset has complete, consistent and integrant properties for further analysis. Dimensionality reduction is this paper is discussed as one of the preprocessing stages using the most popular matrix factorization methods. We have also made an experiment to show how the dimensionality reduction algorithms can overcome any constrained computational resources and prove better performance and efficiency for such demanding recommendation systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. https://grouplens.org/datasets/movielens/. Accessed 18 Oct 2019

  2. https://movielens.org/. Accessed 18 Oct 2019

  3. Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015)

    Google Scholar 

  4. https://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/. Accessed 20 Oct 2019

  5. Al-Garadi, M.A., Mohamed, A., Al-Ali, A., Du, X., Guizani, M.: A survey of machine and deep learning methods for Internet of Things (IoT) security. arXiv preprint (2018)

    Google Scholar 

  6. Vozalis, M.G., Konstantinos, G.M.: A recommender system using principal component analysis. In: Published in 11th Panhellenic Conference in Informatics, pp. 271–283. Citeseer (2007)

    Google Scholar 

  7. Juvonen, A., Sipola, T., Hämäläinen, T.: Online anomaly detection using dimensionality reduction techniques for HTTP log analysis. Comput. Netw. 91, 46–56 (2015)

    Article  Google Scholar 

  8. Lee, Y.J., Yeh, Y.R., Wang, Y.C.F.: Anomaly detection via online oversampling principal component analysis. IEEE Trans. Knowl. Data Eng. 25(7), 1460–1470 (2013)

    Article  Google Scholar 

  9. Bokde, D., Girase, S., Mukhopadhyay, D.: Matrix factorization model in collaborative filtering algorithms: a survey. In: Proceedings of 4th International Conference on Advances in Computing, Communication and Control, pp. 136–146 (2015)

    Google Scholar 

  10. Zhou, X., He, J., Huang, G., Zhang, Y.: SVD-based incremental approaches for recommender systems. J. Comput. Syst. Sci. 81(4), 717–733 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., Jararweh, Y.: A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 9th International Conference on Information and Communication Systems (ICICS), Irbid, pp. 102–106 (2018)

    Google Scholar 

  12. Yang, H., Wang, Y.X., Xia, J.J., Zhang, Y.: China GDP prediction on traffic and transportation by PCA. In: Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management, Singapore, pp. 388–394 (2018)

    Google Scholar 

  13. Vasan, K., Surendiran, B.: Dimensionality reduction using principal component analysis for network intrusion detection. Perspect. Sci. 8, 510–512 (2016)

    Article  Google Scholar 

  14. Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018)

    Article  Google Scholar 

  15. Deldjoo, Y., Constantin, M.G., Eghbal-Zadeh, H., Ionescu, B., Schedl, M., Cremonesi, P.: Audiovisual encoding of multimedia content for enhancing movie recommendations. In: Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, 2–7 October 2018, pp. 455–459 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Hagar ElFiky, Wedad Hussein & Rania El Gohary

Authors
  1. Hagar ElFiky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wedad Hussein
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rania El Gohary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hagar ElFiky .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

ElFiky, H., Hussein, W., El Gohary, R. (2020). Improving the Data Quality of the MovieLens Dataset Using Dimensionality Reduction Techniques. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_51

Download citation

Publish with us

Policies and ethics

Search

Navigation