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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-030-19738-4_9
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