Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Practical Incremental Method to Train Deep CTR Models
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2009.02147.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:52:50.680086Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-04T22:32:18.196981Z
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 e2b1b9d5-c98c-46cb-95bf-98720113d1e3 · inbound
Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning cites this paper.
Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning A Practical Incremental Method to Train Deep CTR Models
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:52:50.680086Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8c9c2649-a37c-4bb4-92d8-4f09f6e35edf · inbound
MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models cites this paper.
MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models A Practical Incremental Method to Train Deep CTR Models
Reference 36
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-04T22:32:18.205359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5f1c7c28-3573-46eb-81c1-35f1d9aa51f4 · inbound
Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation cites this paper.
Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation A Practical Incremental Method to Train Deep CTR Models
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:14:41.885947Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.