Skip to main content
Springer Nature Link
Log in

Recommender Systems Algorithm Selection Using Machine Learning

  • Conference paper
  • First Online:
Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN 2021)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 3))

  • 937 Accesses

Abstract

This article delivers a methodology for recommender system algorithm selection using a machine learning classifier. Initially, statistical data from real collaborative filtering recommender systems have been collected to form the basis for a synthetic dataset since a real meta dataset doesn’t exist. Once the dataset has been developed a classifier can be applied to predict which recommender system among a range of algorithms will predict better for a given dataset. The experimental evaluation shows that tree-based approaches such as Decision Tree and Random Forest work well and provide results with high accuracy and precision. We can conclude that machine learning can be used along with a meta dataset comprised of statistical information in order to predict which recommender system algorithm will provide better recommendations for similar datasets.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  2. Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)

    Article  Google Scholar 

  3. Polatidis, N., Georgiadis, C.K.: A multi-level collaborative filtering method that improves recommendations. Expert Syst. Appl. 48, 100–110 (2016)

    Article  Google Scholar 

  4. Polatidis, N., Georgiadis, C.K., Pimenidis, E., Mouratidis, H.: Privacy-preserving collaborative recommendations based on random perturbations. Expert Syst. Appl. 71, 18–25 (2017)

    Article  Google Scholar 

  5. Abdullah, N., Xu, Y., Geva, S.: Integrating collaborative filtering and matching-based search for product recommendations. J. Theor. Appl. Electron. Commer. Res. 8(2), 34–48 (2013)

    Article  Google Scholar 

  6. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)

    Article  Google Scholar 

  7. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. ACM SIGIR Forum 51(2), 227–234 (2017)

    Article  Google Scholar 

  8. Bobadilla, J., Ortega, F., Hernando, A.: A collaborative filtering similarity measure based on singularities. Inf. Process. Manage. 48(2), 204–217 (2012)

    Article  Google Scholar 

  9. Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The adaptive web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_12

    Chapter  Google Scholar 

  10. Anand, D., Bharadwaj, K.K.: Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst. Appl. 38(5), 5101–5109 (2011)

    Article  Google Scholar 

  11. Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl.-Based Syst. 56, 156–166 (2014)

    Article  Google Scholar 

  12. Son, L.H.: HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems. Exp. Syst. Appl. Int. J. 41(15), 6861–6870 (2014)

    Article  Google Scholar 

  13. Toledo, R.Y., Mota, Y.C., Martínez, L.: Correcting noisy ratings in collaborative recommender systems. Knowl.-Based Syst. 76, 96–108 (2015)

    Article  Google Scholar 

  14. Wang, W., Zhang, G., Lu, J.: Collaborative filtering with entropy-driven user similarity in recommender systems. Int. J. Intell. Syst. 30(8), 854–870 (2015)

    Article  Google Scholar 

  15. Bobadilla, J., Alonso, S., Hernando, A.: Deep learning architecture for collaborative filtering recommender systems. Appl. Sci. 10(7), 2441 (2020)

    Article  Google Scholar 

  16. Nikolakopoulos, A.N., Berberidis, D., Karypis, G., Giannakis, G.B.: Personalized diffusions for top-n recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 260–268 (2019)

    Google Scholar 

  17. Pang, B., Yang, M., Wang, C.: A novel top-N recommendation approach based on conditional variational auto-encoder. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11440, pp. 357–368. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16145-3_28

    Chapter  Google Scholar 

  18. Gazdar, A., Hidri, L.: A new similarity measure for collaborative filtering based recommender systems. Knowl.-Based Syst. 188, 105058 (2020)

    Article  Google Scholar 

  19. Patra, B.K., Launonen, R., Ollikainen, V., Nandi, S.: A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl.-Based Syst. 82, 163–177 (2015)

    Article  Google Scholar 

  20. Wang, Y., Deng, J., Gao, J., Zhang, P.: A hybrid user similarity model for collaborative filtering. Inf. Sci. 4(18), 102–118 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, BN2 4GJ, UK

    Nikolaos Polatidis & Stelios Kapetanakis

  2. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, BS16 1QY, UK

    Elias Pimenidis

Authors
  1. Nikolaos Polatidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stelios Kapetanakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elias Pimenidis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elias Pimenidis .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Polatidis, N., Kapetanakis, S., Pimenidis, E. (2021). Recommender Systems Algorithm Selection Using Machine Learning. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_39

Download citation

Publish with us

Policies and ethics