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AI Research
arXiv CS.AI·Jun 10·5 min read
Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
TL;DR
Digital marketplaces often rely on popularity-based signals for ranking, which can lead to overfitting and poor performance for new items. To address this, a method called Representation Curriculum (RC) stages the use of features, initially focusing on content-based merit signals before introducing popularity signals.
✦ Why It Matters
Engineers can implement Representation Curriculum to improve ranking systems' performance for new items without sacrificing established item rankings.
Key Takeaways
- Adopt stagewise training to enhance ranking robustness.
- Focus on content-based signals before introducing historical data.
- Evaluate performance trade-offs in cold-start scenarios.
- Utilize RC to improve generalization in digital marketplaces.
How It Works
RC begins by training models on content-based merit signals, which assess the intrinsic quality of items. As training progresses, exposure-dependent signals are introduced, but the model is anchored to the initial content representation.
This staged approach mitigates the risk of overfitting to historical data and ensures that the model remains sensitive to the actual quality of new items.