Different history items need different processing depth to improve some predictions
The study tests giving different past items more or less reasoning time to predict next items.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Zhanshuo Cao, Jiakai Tang, Yuqi Zhou, Sunhao Dai, Jun Xu
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Existing models treat every history item the same. The study found that most items need little analysis, while a small share require full depth. A flexible, per-item depth approach sometimes beats a fixed depth in some cases.
Why it matters
Treating items differently could save resources and avoid overworking routine signals while spending more effort on unusual items may improve accuracy when history is uncertain.
User histories mix routine actions with rare or changing signals. A fixed depth wastes effort on obvious cases and may miss subtle ones in practice.
In commerce, skip extra processing for typical purchases but spend more on unusual items to better track changing tastes over time and across different sites.
What remains uncertain
Results vary by dataset and setup; gains are not guaranteed across brands, sites, or audiences outside trials in real settings.
Original sources · 1
- Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation ↗arXiv · 2026-10-21
Check the original paper for its authors, methods, version and access terms.
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