Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay

Di Bai; Feng Han; Zhenwei Tang; Jintao Liu; Luoshu Wang; Jialu Liu

cs.IRAug 16, 2026

Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay

Authors: Di Bai, Feng Han, Zhenwei Tang, Jintao Liu, Luoshu Wang, Jialu Liu

Organizations: Google LLC Mountain View, USA · Google LLC Toronto, Canada · Google LLC New York, USA

Abstract

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 recommendation feed with hundreds of millions of daily and billions of monthly active users. SDF targets both mechanisms with complementary filters, each powered by a learned model: a relational staleness model that detects supersession between item pairs, and a predicted traffic ratio (PTR) model that forecasts relevance decay from the item's content, trained on lifetime visit traffic. Applied via disjunction upstream of the ranking stage, SDF prunes stale candidates, measurably reducing downstream serving costs. Online experiments demonstrate that these filters significantly reduce the prevalence of stale content while improving user engagement. Over a two-year production deployment, user-filed staleness reports (in-product user feedback) declined by 54.9% relative to the pre-deployment baseline, establishing SDF as a robust and scalable paradigm for resolving content staleness at industrial scale.

Figures & tables

Panel of Figure 1 — Figure 1. Two staleness mechanisms. Top: Supersession. Each new update on the Ohtani signing renders prior coverage stale. Bottom: Relevance decay. Time-sensitive items naturally lose value and become stale as they near the end of their useful lifespan, without any superseding counterpart. Two illustrations of content staleness. The top panel is a supersession timeline in which successive news items about Shohei Ohtani's team signing are placed along a left-to-right time axis, with arrows marking that each later item renders the earlier coverage stale. The bottom panel shows user-visit-over-time curves illustrating relevance decay: a time-sensitive item spikes sharply and then goes stale within a day, while a slower-decaying item rises and falls over a longer period before going stale, each marked stale where its traffic tails off.

Figure 2. SDF end-to-end workflow. A candidate item $d_{t}$ (arriving at time $t$ ) is evaluated in parallel. Top: Relational staleness filter for supersession. A student model predicts pairwise staleness, yielding $S_{\mathrm{supersession}}(d_{t})$ . Bottom: Intrinsic staleness filter for relevance decay. A multimodal model predicts per-horizon traffic ratios from the item’s content, yielding $S_{\mathrm{decay}}(d_{t};t^{\prime})$ for decision time $t^{\prime}$ . The two outputs are OR-fused into $S(d_{t};t^{\prime})$ ; items flagged ( $S(d_{t};t^{\prime})=1$ ) are filtered, while the rest remain eligible for recommendation and pass to downstream serving. Block diagram of the SDF end-to-end workflow with two parallel branches evaluating a candidate item. The top branch is the relational staleness filter: a student model classifies whether newer topic-related items render the candidate stale, yielding a supersession indicator. The bottom branch is the intrinsic staleness filter: the item's text, image, and video are combined in a multimodal encoder and passed to a multi-task PTR network that predicts a series of per-horizon traffic ratios; these are then combined into a decay indicator at the current decision time. The two indicators are OR-fused into a joint staleness indicator that determines whether the item stays eligible for recommendation or is excluded from the candidate set.

	Acc.	Prec.	Rec.	F1
Disc	83.71	83.68	85.15	84.41
Disc + NLI	83.86	84.25	84.65	84.45
Gen	83.37	81.31	88.14	84.59
Gen + NLI	84.16	84.31	85.28	84.79

Table 1. Relational staleness offline evaluation. Disc (discriminative): class-only supervision; Gen (generative): CoT-style rationale supervision; NLI: auxiliary pair-relational training. Per column, bold marks the best value and underline the second best.

Article R: The Oakland Athletics are looking to continue their winning streak against the struggling Texas Rangers…

Article A: Rangers hope to have stabilized following series victory over Oakland. The Texas Rangers came away with a series victory …

Output: Article R is about the Oakland Athletics’ goal of winning a series against the Texas Rangers. Article A reports on the outcome of that series , making Article R outdated. Therefore, Stale .

Article R: Fixed rate cliff looms over Australian housing market. New CoreLogic report sheds light on emerging risk…

Article A: CoreLogic has released a new report unpacking how the housing market has evolved in the three years since COVID was declared a global pandemic.…

Output: Both articles discuss the Australian housing market. Article R focuses on the fixed rate cliff, while Article A provides an overview of the market’s performance over the past three years. Since Article A does not mention the fixed rate cliff , it does not make Article R stale. Therefore, Fresh .

Table 2. CoT-style decoded outputs from the relational staleness filter. “Article R” is the relevant existing item; “Article A” is the arriving new item.

Figure 3. PTR stale-item classification quality at varying rating-majority cuts; the deployed classifier (

\theta\!=\!0.9

) is held fixed. All metrics rise because tighter agreement isolates higher-confidence cases. Grouped bar chart of PTR stale-item classification quality, reporting accuracy, precision, recall, and F1 at three rating-agreement cuts: Majority (at least 3 of 5 raters), Strong (at least 4 of 5), and Unanimous (5 of 5). Within each metric the bars increase as agreement tightens; F1 rises from about 78 percent at the Majority cut to about 88 percent at the Unanimous cut, and precision rises from about 88 percent to about 97 percent.

Filter	Promoted	Demoted	$\Delta_{\mathrm{stale}}$
Baseline	$4.00\pm 0.86\%$	$4.10\pm 0.87\%$	$-0.10\pm 1.22\%$
Relational	$1.68\pm 0.81\%$	$5.87\pm 2.11\%$	$-4.19\pm 2.25\%$
Intrinsic	$7.90\pm 1.67\%$	$11.00\pm 1.94\%$	$-3.10\pm 2.56\%$
SDF	$4.33\pm 0.89\%$	$11.24\pm 2.04\%$	$\mathbf{-6.91}\pm 2.23\%$

Table 3. Prevalence delta of SDF and its component filters individually, alongside an age-cutoff + engagement-cliff baseline. Negative

\Delta_{\mathrm{stale}}

means the promoted set is less stale than the demoted set. For

\Delta_{\mathrm{stale}}

, bold marks the best result.

Panel of Figure 4 — Figure 4. User-filed staleness reports over the two-year SDF deployment in Discover, by mechanism: total (blue), supersession (red), decay (yellow). Top: 7-day rolling average. Bottom: 28-day rolling average. Two line charts of user-filed staleness report volumes over the two-year SDF deployment, each plotting three curves: total reports (blue), supersession reports (red), and decay reports (yellow), against time on the horizontal axis. The top chart shows the 7-day rolling average and the bottom chart the 28-day rolling average. All three curves trend downward over the window, with the total and supersession curves declining most, indicating fewer staleness reports after deployment.

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