Paper Citation Record · LEDGER

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

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 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 22 of 22 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 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:38:28.364742Z

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

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.

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.

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.

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.

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.

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.

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.

Observation 1046995d-c50d-4b8f-a632-853c1fae7cf7 · inbound

Stay Hungry, Stay Foolish: On the Extended Reading Articles Generation with LLMs cites this paper.

Stay Hungry, Stay Foolish: On the Extended Reading Articles Generation with LLMs How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:38:28.364742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e631827b-ee57-474c-8bb2-e0d1b5e5ec68 · inbound

A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms cites this paper.

A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:07:57.912449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77a30c49-463b-4ec2-b4f3-36dc77fc3f0c · inbound

Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation cites this paper.

Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:00.788414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3728317-76a2-40e0-a3d3-d499adc936c6 · inbound

SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation cites this paper.

SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:16:02.098194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8186f026-f37a-4dde-bc66-e88eb6bba882 · inbound

LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis cites this paper.

LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T21:35:40.219765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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.

Observation 459d9066-a26c-4c0e-9583-c09bc92c9a39 · inbound

LettinGo: Explore User Profile Generation for Recommendation System cites this paper.

LettinGo: Explore User Profile Generation for Recommendation System How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T18:57:42.306245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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

Observation af4594a3-6590-43ca-94bd-0069a9d7c49a · inbound

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging cites this paper.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T14:24:17.080227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.