Abstract
In the context of news recommendations, many time-aware approaches were proposed. These approaches have tried to capture the recency of news with respect to their short life span, by using either decaying weights on past articles or even forgetting them. However, most of these approaches have missed to consider sessions, which encapsulate inside them the articles that a user has interacted with in a short time period. In this paper, we provide news recommendations based on user sessions to reveal their short-term intentions. We also combine content-based with collaborative filtering to deal with the severe data sparsity problem that exists in our real-life data set. We have experimentally seen that the users' interests evolve over time and that our strategies can adapt fast to these changes.