Paper Citation Record · LEDGER

How Can Recommender Systems Benefit from Large Language Models: A Survey

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.05817.

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

pith.paper-citation-record.v1
2306.05817 v6

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 2026-08-06T13:06:32.425492Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T15:34:48.798253Z

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 2572aef2-445c-408f-a8c1-2c23a2ba9366 · inbound

A Survey on the Memory Mechanism of Large Language Model based Agents cites this paper.

A Survey on the Memory Mechanism of Large Language Model based Agents How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:21:39.830751Z

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.

Observation d9bb156e-7077-4a25-b77e-4046b99350b9 · inbound

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations cites this paper.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T13:06:32.425492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0b9944c-e6cc-4681-9854-eeccf7f37c99 · inbound

Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation cites this paper.

Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T11:02:13.416000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95a048c5-68e5-46e1-9721-31a4df91d568 · inbound

Benchmark Leakage Trap: Can We Trust LLM-based Recommendation? cites this paper.

Benchmark Leakage Trap: Can We Trust LLM-based Recommendation? How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T23:32:08.633592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d343495-a5ab-4a63-89a0-aa822fd993dc · inbound

Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking cites this paper.

Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:34:48.799736Z

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.