SAGERec: Sampling and Gating for Enhanced Long-Tail Item Recommendations
Abstract
Recommendation systems are an integral part of daily life, influencing how people interact with and access information. The content recommended to users shapes their perceptions, making it crucial to eliminate biases that could negatively impact those perceptions. One such bias is the popularity bias which causes the long-tail item recommendation problem, where systems tend to favor popular items while overlooking less popular yet relevant ones.
Fields of Study & Subjects
Recommender Systems and Techniques
Advanced Bandit Algorithms Research
Sentiment Analysis and Opinion Mining
Publication details
- Journal / source
- Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Pages
- 16–26
- Publication date
- 16 February 2026