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Paper Citation Record · LEDGER

Computation Resource Allocation Solution in Recommender Systems

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.02259.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2103.02259 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:10:56.737713Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T23:33:01.357549Z

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 7112391c-2829-4f25-a4e1-eb82dbbe4e4b · inbound

Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling cites this paper.

Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling Computation Resource Allocation Solution in Recommender Systems

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T18:10:56.737713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:10:56.737713Z digest=sha256:d9a3aaf5aac1627e441cc4f2ccfbfa46eb897e94d6a038b7ec9492262d5ebf48

Observation f43ba0ca-21d6-4ae6-8937-859e380036bd · inbound

Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling cites this paper.

Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling Computation Resource Allocation Solution in Recommender Systems

Reference 61

Resolution
verified exact
local_arxiv, observed 2026-08-07T23:33:01.362738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T23:33:01.121097Z digest=sha256:f3fc30812f547644dd4c9535eac55303b2548c3471cee85b99de215732b32fa7