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

FRANCO BERDUN; MARCELO ARMENTANO; EMILIO BONGIORNO; ARIEL MONTESERIN; LUIS MARÍA COUSSIRAT

Guadalajara

Conferencia; 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 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.