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Paper Citation Record · LEDGER

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

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 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 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:03:43.722005Z

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-15T07:21:39.440092Z digest=sha256:abc8f84097a11d9e2f9b9dca46c939f2073e599c92e80bbc17c9453f5c43f090

Observation acfb23e1-9eec-40ee-9ede-3ad080494d4f · inbound

LIBER: Lifelong User Behavior Modeling Based on Large Language Models cites this paper.

LIBER: Lifelong User Behavior Modeling Based on Large Language Models How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-12T15:03:43.722005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:03:43.722005Z digest=sha256:03e8ab1f41b91d1286313c9583714ebd35ba35411fd31e7ac9e5feb935531397

Observation c1a9f347-8a8b-4f5a-a22a-fca6c247dd10 · inbound

Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs cites this paper.

Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-11T14:41:33.499974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:41:33.499974Z digest=sha256:21efda8f9650cd242eeb08910f231bcb66023e24a4bd78fc400abd5c74e44ac9

Observation 059abde5-c8c5-44e6-8315-8818deeebf49 · inbound

Large Language Model Enhanced Recommender Systems: A Survey cites this paper.

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

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T13:11:48.631444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:11:48.631444Z digest=sha256:997e3872af36f9f96d5dab7e28c2b6639aa76695f2863891af06a670abd8a33f

Observation 0399b5bd-97b0-4751-ac98-35ed3d108267 · inbound

Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach cites this paper.

Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T05:59:18.692585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:59:18.692585Z digest=sha256:2dac9a7812a5654fbd6f4e1cab482971d13d120de1445bc91eee858637de9f72

Observation 0fd91542-3e8e-4aee-89c3-04aa5f1bdaaa · inbound

Revisiting Language Models in Neural News Recommender Systems cites this paper.

Revisiting Language Models in Neural News Recommender Systems How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T18:22:03.866846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:22:03.866846Z digest=sha256:9963b0ace4db2a41174d2bdfde36ea2690863ff5338b84b95cfb5b4b6b8549ab

Observation b45d60de-ce33-4bf5-a395-1e3a8adc0f75 · inbound

Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems cites this paper.

Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T17:59:56.233290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:59:56.233290Z digest=sha256:07cb2afecd23e8e10a2cfb0dc48334649a8f215740f42ff5faaa87041810c7ca

Observation 44f29681-f563-4cd3-8850-e8524b51eac2 · inbound

Large Language Model driven Policy Exploration for Recommender Systems cites this paper.

Large Language Model driven Policy Exploration for Recommender Systems How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T15:39:56.460525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:39:56.460525Z digest=sha256:8f3b758660245eac63fb93e9e3477e8add556a2f95832932f8ea6c7271bc6e89

Observation b7770e29-cc52-476c-b34d-2b03a1b3b900 · inbound

Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation cites this paper.

Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T06:03:43.553644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:03:43.553644Z digest=sha256:e8276fee299effa1e402088a13b3e41a0ca4a112cf3ba3936e843f2422c18db1

Observation 9ddd6889-3d65-4558-8c82-f90757c68a8e · inbound

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking cites this paper.

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T05:40:12.634282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:40:12.634282Z digest=sha256:3e2d23e6af691aba0a6381a8cb9d3c9e89f821ac7c63bed269ff78aa22105745

Observation dd13056c-ae68-464d-9041-459f1206fa49 · inbound

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs cites this paper.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-06T19:22:29.890345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:22:29.890345Z digest=sha256:6af74d42f49338f6e9463800e4b89227790ef4d9a8b2c9e5036db1c9ca9a3f6b

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.

source=pdf_text observed=2026-08-06T13:06:32.425492Z digest=sha256:d5238218fa851c9fc234a01148dfe944fe06dcb2fd21319cf080dea3b2ae38f1

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.

source=pdf_text observed=2026-08-06T11:02:13.416000Z digest=sha256:3c7a2faa63e768ba68de2703457826c726a8f9c21fedabbb63442aa17a842d82

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.

source=arxiv_source observed=2026-08-02T23:32:08.633592Z digest=sha256:9724450d4e1e58c77158b8d3597e9546bd33b8fa6bd19965e8431a77954c413f

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T15:15:52.806007Z digest=sha256:de2e11c086aae4cb6a482caf166e992af7c65eeade8880f085e786f756250be5