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Paper Citation Record · LEDGER

Data Poisoning Attacks to Deep Learning Based Recommender Systems

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2101.02644.

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

pith.paper-citation-record.v1
2101.02644 v2

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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:16:46.891644Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T18:42:29.074463Z

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 76f044b5-7a61-4638-ad68-824f3e7df69e · inbound

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS cites this paper.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Data Poisoning Attacks to Deep Learning Based Recommender Systems

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T11:16:46.891644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:16:46.891644Z digest=sha256:7610c9738fab7af9d448039b9c5209eba075d54d2da442f25fdd4f07bb0f4c70

Observation 7c9a3b69-60d6-47fb-9766-5cd219776fa3 · inbound

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges cites this paper.

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges Data Poisoning Attacks to Deep Learning Based Recommender Systems

Reference 89

Resolution
verified exact
arxiv_id, observed 2026-06-28T18:42:29.075857Z

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.

source=pdf_text observed=2026-06-28T18:41:06.636352Z digest=sha256:3a1b20fba7d71a878b5286ea983d037aad190e209d0e94f04ff4ca7ba56e8c38