Abstract
User recommendation plays an important role in microblogging systems since users connect to these networks to share and consume content. Finding relevant users to follow is then a hot topic in the study of social networks. Microblogging networks are characterized by having a large number of users, but each of them connects with a limited number of other users, making the graph of followers to have a low degree. One of the main problems of approaching user recommendation with a learning-based approach in low-degree networks is the problem of extreme class imbalance. In this article, we propose a balancing scheme to face this problem, and we evaluate different classification algorithms using as features classical metrics for link prediction. We found that the learning-based approach outperformed individual metrics for the problem of user recommendation in the evaluated dataset. We also found that the proposed balancing approach lead to better results, enabling a better identification of existing connections between users.
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This work was partially supported by research project PICT-2014-2750.
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Armentano, M.G., Monteserin, A., Berdun, F., Bongiorno, E., Coussirat, L.M. (2018). User Recommendation in Low Degree Networks with a Learning-Based Approach. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_22
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