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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Burduk R, Kurzyński M (2006) Two-stage binary classifier with fuzzy-valued loss function. Pattern Anal Appl 9(4):353–358
Buza K (2014) Feedback prediction for blogs. In: Data analysis, machine learning and knowledge discovery, pp 145–152. Springer
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
Buza K, Nanopoulos A, Nagy G (2015) Nearest neighbor regression in the presence of bad hubs. Knowl-Based Syst 86:250–260
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
Jackowski K, Wozniak M (2010) Method of classifier selection using the genetic approach. Expert Syst 27(2):114–128
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
Kaur M, Verma P (2016) Comment volume prediction using regression. Int J Comput Appl 151(1): 1–9
Rendle S (2010) Factorization machines. In: 2010 IEEE 10th international conference on data mining (ICDM), pp 995–1000. IEEE
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
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
Uddin MT (2015) Automated blog feedback prediction with Ada-Boost classifier. In: 2015 international conference on informatics, electronics & vision (ICIEV), pp 1–5. IEEE
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
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
DOI: https://doi.org/10.1007/978-3-030-19738-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19737-7
Online ISBN: 978-3-030-19738-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)