Back to Projects
RecSys Challenge 2024 – News Recommendation System Development
Recommender Systems
Machine Learning
Large-Scale Data
Algorithm Optimization
News Recommendation
Vienna University of Technology • March 2024 - June 2024
Algorithm Developer
Project Overview
Collaborated in a team of five to implement and improve three algorithms, including NRMS, Standard VAE, and SAR, for large-scale news recommendation. Focused on addressing beyond-accuracy metrics such as diversity, serendipity, and novelty to align with the ACM RecSys 2024 objectives.
Challenges
- •Working with the large-scale Ekstra Bladet News Recommendation Dataset (600M impressions)
- •Improving baseline and advanced algorithms to meet beyond-accuracy objectives like diversity and serendipity
- •Optimizing training processes and addressing computational constraints using smaller datasets
- •Evaluating and comparing the performance of algorithms under strict challenge criteria
Key Achievements
- •Successfully implemented and improved three algorithms: NRMS, Standard VAE, and SAR
- •Enhanced SAR algorithm, achieving notable improvements in ranking metrics such as NDCG and precision
- •Generated reproducible results using advanced preprocessing and data augmentation techniques
- •Submitted competitive predictions to the RecSys Challenge leaderboard
Technologies Used
NRMS
Standard VAE
SAR
Python
Machine Learning
Recommendation Metrics