Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2209.05310.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T19:11:32.393914Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T16:20:16.326294Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation faf91269-c3d0-4865-a09d-9e549d98abe3 · inbound
Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google cites this paper.
Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T19:11:32.393914Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ec0149e4-686e-460f-99cb-71f1f3afb157 · inbound
Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking cites this paper.
Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T04:12:56.232629Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 273ac910-3aba-4b73-89d2-123d48953f4f · inbound
Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training cites this paper.
Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
Reference 1
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-05T16:20:16.333810Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation dac832cc-0a3b-4ebe-b143-afa841b6e4f8 · inbound
Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems cites this paper.
Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T18:42:47.482946Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.