User recommendation in low degree networks with a learning-based approach

ARMENTANO, MARCELO G.; MONTESERIN, ARIEL J.; BERDUN, FRANCO D.; BONGIORNO, EMILIO; COUSSIRAT, LUIS MARÍA

Guadalajara

Conferencia; MICAI 2018 - 17th Mexican International Conference on Artificial Intelligence; 2018

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 learningbased 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.