DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation
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.
Authors
- Ludovico Boratto (ORCID: https://orcid.org/0000-0002-6053-3015)
- Claudio Pomo (ORCID: https://orcid.org/0000-0001-5206-3909)
- Giovanni Maria Biancofiore (ORCID: https://orcid.org/0000-0003-1317-8695)
- Vito Walter Anelli (ORCID: https://orcid.org/0000-0002-5567-4307)
- Fedelucio Narducci (ORCID: https://orcid.org/0000-0002-9255-3256)
- Antonio Ferrara (ORCID: https://orcid.org/0000-0002-1921-8304)
- Davide Abbattista
- Paolo Masciullo
- Antonio De Candia
- Riccardo Fusco
- Marco Valentini
Institutions
- University of Cagliari (IT)
- Polytechnic University of Bari (IT)
Publication Details
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1145/3687151.3687153
- Citations
- 2
- Primary Topic
- Recommender Systems and Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.56