Soft Curriculum Learning for Optimizing Fresh and Generalized Recommendations
Large-scale recommender systems, particularly short-form video platforms, are often bottlenecked by massive popularity feedback loops. In such environments, as models recommend popular items, they generate an overwhelming amount of skewed training data for "head" items. This creates a self-reinforcing cycle where retrieval and ranking models memorize "head" item patterns at the expense of generalizing across the vast "tail" of the catalogue. While Curriculum Learning (CL) offers a powerful mechanism to break this feedback loop by systematically exposing models to progressively more difficult and less frequent examples, its adoption in industrial recommendation has been hampered by hardwar...
| Authors | Arnab Bhadury, Siyan Zheng, Anlan Yu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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