Adaptive User Engagement Evaluation via Multi-task Learning

Abstract

User engagement evaluation task in social networks has recently attracted considerable attention due to its applications in recommender systems. In this task, the posts containing users' opinions about items, e.g., the tweets containing the users' ratings about movies in the IMDb website, are studied. In this paper, we try to make use of tweets from different web applications to improve the user engagement evaluation performance. To this aim, we propose an adaptive method based on multi-task learning. Since in this paper we study the problem of detecting tweets with positive engagement which is a highly imbalanced classification problem, we modify the loss function of multi-task learning algorithms to cope with the imbalanced data. Our evaluations over a dataset including the tweets of four diverse and popular data sources, i.e., IMDb, YouTube, Goodreads, and Pandora, demonstrate the effectiveness of the proposed method. Our findings suggest that transferring knowledge between data sources can improve the user engagement evaluation performance.