DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation
Year: 2024
Type: conference-paper
Abstract: In today’s era of information overload, personalized news recommendation systems are crucial for connecting users with relevant content. The dynamic nature of user interests and the fleeting popularity of news articles pose significant challenges to accurate prediction. For this reason, the RecSys 2024 Challenge aims to inspire innovative solutions in this field. This study presents DIVAN (Deep-Interest Virality-Aware Network), our solution for the RecSys 2024 Challenge, combining a Deep Interest Network (DIN) for personalized user interest representation with a Virality-aware Click Predictor that utilizes temporal features to estimate click probability based on news popularity. A user-specific weight balances the influence of DIN and virality-based predictions, enhancing personalization and accuracy. Experiments on the Ekstra Bladet dataset from the Challenge demonstrate how promising DIVAN is in accuracy and beyond-accuracy performance.
Institutions: Polytechnic University of Bari, University of Cagliari
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4403319935