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Paper Citation Record · LEDGER

LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.03988.

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

pith.paper-citation-record.v1
2405.03988 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:39:58.357864Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T12:04:09.525602Z

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 21fd6e8f-391e-427b-b4d8-4028039b2342 · inbound

GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou cites this paper.

GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T15:39:58.357864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:58.357864Z digest=sha256:9b3d1dd7414ac016aed681afd6451b3392d1e22c5a7204e612d9b5ea4f94509d

Observation acd1f83e-c058-408e-8fa7-40b86588e585 · inbound

Towards Comprehensible Recommendation with Large Language Model Fine-tuning cites this paper.

Towards Comprehensible Recommendation with Large Language Model Fine-tuning LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T22:06:55.190201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:06:55.190201Z digest=sha256:4de93a9a106326082bb2526626e60c3e7994ca82cf6b792e1a567f53be1373f4

Observation 4b9aaeee-e468-4996-9e48-dc5b75bea06b · inbound

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning cites this paper.

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T16:15:27.385459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:15:27.385459Z digest=sha256:c53de45c472ab9f7babbba6aae759a5778911098743bb64e092d60f26bfc0bf5

Observation 33f8201f-5030-4835-91ae-a9c7f60c6089 · inbound

Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders cites this paper.

Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-21T12:04:09.528287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-21T12:00:32.942950Z digest=sha256:2775b7b9f55e18286249439fdf90a5435ef848455516f6c17a9ea893f811eebd

Observation 32c8c2df-f254-4e5c-9e6e-66d082451c9b · inbound

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations cites this paper.

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:06:04.736948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T04:09:10.125285Z digest=sha256:8883fd052e19e9c5b739c33a49b44c0890f148f6fd31efba2b9835e1ebe321bf