DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation
Authors: 
Antonio Ferrara, 
Marco Valentini, Paolo Masciullo, Antonio De Candia, Davide Abbattista, Riccardo Fusco, 
Claudio Pomo, 
Vito Walter Anelli, Giovanni Maria Biancofiore, 
Ludovico Boratto, 
Fedelucio NarducciAuthors Info & Claims
Pages 12 - 16
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.
References
[1]
Yuhan Ding, Bang Wang, Xiangyang Cui, and Minghua Xu. 2024. Popularity prediction with semantic retrieval for news recommendation. Expert Syst. Appl. 247 (2024), 123308. https://doi.org/10.1016/J.ESWA.2024.123308
[2]
Chong Feng, Muzammil Khan, Arif Ur Rahman, and Arshad Ahmad. 2020. News Recommendation Systems - Accomplishments, Challenges & Future Directions. IEEE Access 8 (2020), 16702–16725. https://doi.org/10.1109/ACCESS.2020.2967792
[3]
Marius Kaminskas and Derek Bridge. 2017. Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Trans. Interact. Intell. Syst. 7, 1 (2017), 2:1–2:42.
[4]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980
[5]
Lei Li, Dingding Wang, Tao Li, Daniel Knox, and Balaji Padmanabhan. 2011. SCENE: a scalable two-stage personalized news recommendation system. In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, Beijing, China, July 25-29, 2011, Wei-Ying Ma, Jian-Yun Nie, Ricardo Baeza-Yates, Tat-Seng Chua, and W. Bruce Croft (Eds.). ACM, 125–134. https://doi.org/10.1145/2009916.2009937
[6]
Nuno Moniz, Luís Torgo, Magdalini Eirinaki, and Paula Branco. 2017. A Framework for Recommendation of Highly Popular News Lacking Social Feedback. New Gener. Comput. 35, 4 (2017), 417–450. https://doi.org/10.1007/S00354-017-0019-X
[7]
Tao Qi, Fangzhao Wu, Chuhan Wu, and Yongfeng Huang. 2021. PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 5457–5467. https://doi.org/10.18653/V1/2021.ACL-LONG.424
[8]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009, Jeff A. Bilmes and Andrew Y. Ng (Eds.). AUAI Press, 452–461. https://www.auai.org/uai2009/papers/UAI2009_0139_48141db02b9f0b02bc7158819ebfa2c7.pdf
[9]
Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). 2022. Recommender Systems Handbook. Springer US. https://doi.org/10.1007/978-1-0716-2197-4
[10]
David Vallet, Shlomo Berkovsky, Sebastien Ardon, Anirban Mahanti, and Mohamed Ali Kâafar. 2015. Characterizing and Predicting Viral-and-Popular Video Content. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015, James Bailey, Alistair Moffat, Charu C. Aggarwal, Maarten de Rijke, Ravi Kumar, Vanessa Murdock, Timos K. Sellis, and Jeffrey Xu Yu (Eds.). ACM, 1591–1600. https://doi.org/10.1145/2806416.2806556
[11]
Jingkun Wang, Yipu Chen, Zichun Wang, and Wen Zhao. 2021. Popularity-Enhanced News Recommendation with Multi-View Interest Representation. In CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 1949–1958. https://doi.org/10.1145/3459637.3482462
[12]
Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2023. Personalized News Recommendation: Methods and Challenges. ACM Trans. Inf. Syst. 41, 1 (2023), 24:1–24:50. https://doi.org/10.1145/3530257
[13]
Zhen Xu, Sergio Escalera, Adrien Pavão, Magali Richard, Wei-Wei Tu, Quanming Yao, Huan Zhao, and Isabelle Guyon. 2022. Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns 3, 7 (2022), 100543.
[14]
Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018, Yike Guo and Faisal Farooq (Eds.). ACM, 1059–1068. https://doi.org/10.1145/3219819.3219823
[15]
Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2021. Open Benchmarking for Click-Through Rate Prediction. In CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 2759–2769. https://doi.org/10.1145/3459637.3482486
Index Terms
- DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation
Recommendations
News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender SystemsNews recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
Modeling and broadening temporal user interest in personalized news recommendation
An experimental study on user interest evolution in real-world recommender systems.Integrating the long-term and short-term reading preferences of users.Selecting news from the user-item affinity graph using absorbing random walk model.Extensive ...
A framework for diversifying recommendation lists by user interest expansion
Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2024
63 pages
ISBN:9798400711275
DOI:10.1145/3687151
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 October 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
RecSys Challenge '24
Acceptance Rates
Overall Acceptance Rate 11 of 15 submissions, 73%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 112Total Downloads
- Downloads (Last 12 months)112
- Downloads (Last 6 weeks)60
Reflects downloads up to 10 Dec 2024
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in