Skip to main content

Factorization Machines for Blog Feedback Prediction

  • 439 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 977)

Abstract

Estimation of the attention that a blog post is expected to receive is an important text mining task with potential applications in various domains, such as online advertisement or early recognition of highly influential fake news. In the blog feedback prediction task, the number of comments is used as proxy for the attention. Although factorization machines are generally well-suited for sparse, high-dimensional data with correlated features, their performance has not been systematically examined in the context of the blog feedback prediction task yet. In this paper, we evaluate factorization machines on a publicly available blog feedback prediction dataset. Comparing the results with other results from the literature, we conclude that factorization machines are competitive with multilayer perceptron networks, linear regression and RBF network. Additionally, we analyze how parameters (feature weights and interaction weights) of factorization machine are learned.

Keywords

  • Blog feedback prediction
  • Factorization machine
  • Machine learning

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-19738-4_9
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-19738-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 1.

Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets/BlogFeedback

References

  1. Burduk R, Kurzyński M (2006) Two-stage binary classifier with fuzzy-valued loss function. Pattern Anal Appl 9(4):353–358

    MathSciNet  CrossRef  Google Scholar 

  2. Buza K (2014) Feedback prediction for blogs. In: Data analysis, machine learning and knowledge discovery, pp 145–152. Springer

    Google Scholar 

  3. Buza K, Galambos I (2013) An application of link prediction in bipartite graphs: personalized blog feedback prediction. In: 8th Japanese-Hungarian symposium on discrete mathematics and its applications, June, pp 4–7. Citeseer

    Google Scholar 

  4. Buza K, Nanopoulos A, Nagy G (2015) Nearest neighbor regression in the presence of bad hubs. Knowl-Based Syst 86:250–260

    CrossRef  Google Scholar 

  5. Jackowski K, Krawczyk B, Woźniak M (2014) Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning. Int J Neural Syst 24(03):1430007

    CrossRef  Google Scholar 

  6. Jackowski K, Wozniak M (2010) Method of classifier selection using the genetic approach. Expert Syst 27(2):114–128

    CrossRef  Google Scholar 

  7. Kaur H, Pannu HS (2018) Blog response volume prediction using adaptive neuro fuzzy inference system. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), pp 1–6. IEEE

    Google Scholar 

  8. Kaur M, Verma P (2016) Comment volume prediction using regression. Int J Comput Appl 151(1): 1–9

    CrossRef  Google Scholar 

  9. Rendle S (2010) Factorization machines. In: 2010 IEEE 10th international conference on data mining (ICDM), pp 995–1000. IEEE

    Google Scholar 

  10. Singh K, Sandhu RK, Kumar D (2015) Comment volume prediction using neural networks and decision trees. In: IEEE UKSim-AMSS 17th international conference on computer modelling and simulation, UKSim 2015, Cambridge, United Kingdom

    Google Scholar 

  11. Suciu M, Lung RI, Gaskó N, Dumitrescu D (2013) Differential evolution for discrete-time large dynamic games. In: 2013 IEEE congress on evolutionary computation (CEC), pp 2108–2113. IEEE

    Google Scholar 

  12. Uddin MT (2015) Automated blog feedback prediction with Ada-Boost classifier. In: 2015 international conference on informatics, electronics & vision (ICIEV), pp 1–5. IEEE

    Google Scholar 

  13. Yamada M, Lian W, Goyal A, Chen J, Wimalawarne K, Khan SA, Kaski S, Mamitsuka H, Chang Y (2015) Convex factorization machine for regression. arXiv preprint arXiv:1507.01073v1

Download references

Acknowledgments

This work was supported by the project no. 20460-3/2018/FEKUTSTRAT within the Institutional Excellence Program in Higher Education of the Hungarian Ministry of Human Capacities.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krisztian Buza .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Buza, K., Horváth, T. (2020). Factorization Machines for Blog Feedback Prediction. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_9

Download citation