Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay
Stale recommendations are a pervasive challenge and a leading source of user complaints on large-scale content platforms. Items lose relevance through two primary mechanisms: supersession, where emerging updates render prior coverage stale, and relevance decay, where an item's informational value naturally diminishes over its lifecycle. Traditional countermeasures serve as crude proxies: age cutoffs poorly reflect actual relevance loss, while engagement heuristics rely on lagging signals, broadly exposing users to stale content before the system adapts. We present SDF (Supersession-Decay Filtering), a staleness filtering system fully deployed in Google Discover, a personalized recommendat...
| Authors | Di Bai, Feng Han, Zhenwei Tang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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