About
This year's challenge focuses on online news recommendation, addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. The challenge will delve into the unique aspects of news recommendation, including modeling user preferences based on implicit behavior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. Furthermore, our challenge embraces the normative complexities, involving investigating the effects of recommender systems on the news flow and whether they resonate with editorial values.
Challenge Task
The Ekstra Bladet RecSys Challenge aims to predict which article a user will click on from a list of articles that were seen during a specific impression. Utilizing the user's click history, session details (like time and device used), and personal metadata (including gender and age), along with a list of candidate news articles listed in an impression log, the challenge's objective is to rank the candidate articles based on the user's personal preferences. This involves developing models that encapsulate both the users and the articles through their content and the users' interests. The models are to estimate the likelihood of a user clicking on each article by evaluating the compatibility between the article's content and the user's preferences. The articles are ranked based on these likelihood scores, and the precision of these rankings is measured against the actual selections made by users.
Evaluation
To evaluate the models, we use several standard metrics in the recommendation field, including the
area under the ROC curve (AUC),
mean reciprocal rank (MRR), and
normalized discounted cumulative gain (nDCG@K) for K shown recommendations.
To address the normative complexities inherent in news recommendations, the test set incorporates
samples specifically designed to assess models based on normative properties.
This includes evaluating models on Beyond-Accuracy Objectives, such as intra-list diversity,
serendipity, novelty, coverage, among others.
The final result is the average of these metrics across all impression logs.
Prizes
The top three teams will receive exciting cash prizes: $3,500 for first place, $2,500 for second, and $1,500 for third. Additionally, a special $2,500 prize will be awarded to the best academic team.
Timeline
The table below presents the comprehensive timeline and critical deadlines relevant to the challenge. It's essential to note that all listed dates and times are based on the Anywhere on Earth (AoE) timezone, marked at 23:59:59.
When? | What? |
---|---|
Start RecSys Challenge
Release dataset |
|
Submission System Open | |
Leaderboard live | |
End RecSys Challenge | |
Final Leaderboard & Winners
EasyChair open for submissions |
|
Code Upload
Upload code of the final predictions |
|
Paper Submission Due | |
Paper Acceptance Notifications | |
Camera-Ready Papers | |
RecSys Challenge Workshop @ACM RecSys 2024 |
Organizers
The challenge is organized by Ekstra Bladet and JP/Politikens Hus A/S ("Ekstra Bladet"), Johannes Kruse1,2, Kasper Lindskow1, Anshuk Uppal2, Michael Riis Andersen2, Jes Frellsen2, Marco Polignano3, Claudio Pomo4 and Abhishek Srivastava5 based on the data provided by Ekstra Bladet.- Ekstra Bladet / JP/Politikens Hus A/S
- Technical University of Denmark
- University of Bari Aldo Moro, Italy
- Politecnico di Bari, Italy
- IIM Visakhapatnam, India
- Johannes.Kruse@jppol.dk
Contributions
Accepted Papers
-
An User Interest Modeling with Diverse Behavior Analyses and Embeddings for Building
Online News Recommendation Systems
Jiangwei Luo, Ye Tang, Shien Song, Haibo Lu -
Leveraging User History with Transformers for News Clicking:
The DArgk Approach
Juan Manuel Rodriguez, Antonela Tommasel -
DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News
Recommendation
Antonio Ferrara, Marco Valentini, Paolo Masciullo, Antonio De Candia, Davide Abbattista, Riccardo Fusco, Claudio Pomo, Vito Walter Anelli, Giovanni Maria Biancofiore, Ludovico Boratto, Fedelucio Narducci -
Enhancing News Recommendation with Real-Time Feedback and Generative Sequence Modeling
Qi Zhang, Jieming Zhu, Jiansheng Sun, Guohao Cai, Ruining Yu, Bangzheng He, Liangbi Li -
Enhancing News Recommendation with Transformers and Ensemble Learning
(🥇)
Kazuki Fujikawa, Naoki Murakami, Yuki Sugawara -
Exploiting Contextual Normalizations and Article Endorsement for News Recommendation
(🥇)
Andrea Alari, Lorenzo Campana, Federico Giuseppe Ciliberto, Saverio Maggese, Carlo Sgaravatti, Francesco Zanella, Andrea Pisani, Maurizio Ferrari Dacrema -
Harnessing Temporal Dynamics and Content: An Ensemble of Gradient Boosting Machines for
News Recommendation
(🥉)
Tomomu Iwai, Akihiro Tomita, Tomoyuki Arai, Hiroki Ogawa, Takuma Saito -
Large Scale Hierarchical User Interest Modeling for Click-through Rate Prediction
(🥈)
Taofeng Xue, Zhimin Lin, Zhijian Zhang, Linsen Guo, Haoru Chen, Mengjiao Bao, Peng Yan -
Leveraging LightGBM Ranker for Efficient Large-Scale News Recommendation Systems
Tetsuro Sugiura, Yosuke Yamagishi, Yodai Kishimoto -
Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the
RecSys Challenge
Lucien Heitz, Sanne Vrijenhoek, Oana Inel
Winners 🏆
Congratulations to the winners of the RecSys Challenge 2024!
🥇 1st Place: :D
- Kazuki Fujikawa (kfujikawa), Naoki Murakami (kami634), Yuki Sugawara (sugawarya), Takuya Akiyama (akiyama).
🥈 2nd Place: BlackPearl
- Peng Yan (contentbetter), Linsen Guo (invalidpointer), Haoru Chen (tilbur), Zhimin Lin (chizhu), Jing Yang (jingy), Zijian Zhang (zachzang), Taofeng Xue (xuetf), Mengjiao Bao (spongebob), Binli Luo (overfitking).
🥉 3rd Place: Tom3TK
- Akihiro Tomita (asato), Tomomu Iwai (tomo426), Tomoyuki Arai (tomoyukiarai), Hiroki Ogawa (kurokurob), Takuma Saito (taksai).
🥇 1st Place [Best Academic Team]: FeatureSalad
Guidelines
We invite researchers and practitioners to submit their work for the RecSys Challenge workshop.
Note that
winning teams must submit papers and sign up for the workshop.
Starting this year, the RecSys submissions have adopted a new template. We will follow the same
new rules that apply to all other types of papers, please follow the Call for Paper
Submission Guidelines.
The topics of interest include, but are not limited to (in alphabetical order):
- Applications of news recommendation
- Benchmarking and evaluation of recommender systems
- Bias in intelligent news systems
- Clickbait, fake news and misinformation detection
- Contributions focused on beyond accuracy, such as fairness, diversity, coverage, etc.
- Cross-domain and multi-modal recommendations
- Dataset analyses and preprocessing techniques
- News categorization, summarization and headline generation
- News content modeling
- News ranking techniques
- News trend and lifecycle
- Novel model architectures for news recommendation
- Privacy protection in news recommendation
- Scalability and efficiency of recommendation algorithms
- User behavior analysis
- User interest modeling
Program
The workshop will take place on Monday, October 14, 2024, at Politecnico
di Bari in Aula Magna on the second floor.
The program is organized into three focused sessions, covering topics such as user modeling,
beyond-accuracy objectives, application of machine learning techniques and traning strategies.
Between sessions, we will have breaks for networking, giving attendees an opportunity to connect and
collaborate. Each talk is allocated 15 minutes for the presentation, followed by 5 minutes for
audience questions and discussion.
For a detailed look at the entire conference, including other tracks and keynotes, see the full
conference schedule. Below is the complete agenda for our workshop:
Session 1: 09:00 - 10:30 | ||
1 | 09:00 - 09:15 | Opening Remarks |
2 | 09:15 - 10:00 | Keynote Speech: Balancing Accuracy and Editorial Values in News
Recommendations
Kasper Lindskow, Ph.D., Head of AI at JP/Politikens Media Group |
3 | 10:00 - 10:15 |
Leveraging User History with Transformers for News Clicking:
The DArgk Approach
Juan Manuel Rodriguez and Antonela Tommasel |
4 | 10:15 - 10:30 |
Recommendations for the Recommenders: Reflections on Prioritizing Diversity
in the
RecSys Challenge
Lucien Heitz, Sanne Vrijenhoek, and Oana Inel |
Coffee Break: 10:30 - 11:15 | ||
Session 2: 11:15 - 12:45 | ||
5 | 11:15 - 11:35 |
Exploiting Contextual Normalizations and Article Endorsement for News
Recommendation
(🥇)
Andrea Alari, Lorenzo Campana, Federico Giuseppe Ciliberto, Saverio Maggese, Carlo Sgaravatti, Francesco Zanella, Andrea Pisani, and Maurizio Ferrari Dacrema |
6 | 11:35 - 11:55 |
Harnessing Temporal Dynamics and Content: An Ensemble of Gradient Boosting
Machines for News Recommendation
(🥉)
Tomomu Iwai, Akihiro Tomita, Tomoyuki Arai, Hiroki Ogawa, and Takuma Saito |
7 | 11:55 - 12:15 |
Large Scale Hierarchical User Interest Modeling for Click-through Rate
Prediction
(🥈)
Taofeng Xue, Zhimin Lin, Zhijian Zhang, Linsen Guo, Haoru Chen, Mengjiao Bao, and Peng Yan |
8 | 12:15 - 12:35 |
Enhancing News Recommendation with Transformers and Ensemble Learning
(🥇)
Kazuki Fujikawa, Naoki Murakami, and Yuki Sugawara |
Lunch Break: 12:45 - 14:30 | ||
Session 3: 14:30 - 16:00 | ||
9 | 14:50 - 15:10 |
Enhancing News Recommendation with Real-Time Feedback and Generative
Sequence Modeling
Qi Zhang, Jieming Zhu, Jiansheng Sun, Guohao Cai, Ruining Yu, Bangzheng He, and Liangbi Li |
10 | 15:10 - 15:30 |
DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in
News
Recommendation
Antonio Ferrara, Marco Valentini, Paolo Masciullo, Antonio De Candia, Davide Abbattista, Riccardo Fusco, Claudio Pomo, Vito Walter Anelli, Giovanni Maria Biancofiore, Ludovico Boratto, and Fedelucio Narducci |
11 | 15:30 - 15:50 |
Leveraging LightGBM Ranker for Efficient Large-Scale News Recommendation
Systems
Tetsuro Sugiura, Yosuke Yamagishi, and Yodai Kishimoto |
12 | 14:30 - 14:50 |
An User Interest Modeling with Diverse Behavior Analyses and Embeddings for
Building Online News Recommendation Systems
Jiangwei Luo, Ye Tang, Shien Song, and Haibo Lu |
13 | 15:50 - 16:00 | Winners' Ceremony (🥇 🥈 🥉) & Closing Remarks
Announcement of the top three teams and the best academic team |