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Paper Citation Record · LEDGER

Graph Foundation Models for Recommendation: A Comprehensive Survey

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2502.08346.

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

pith.paper-citation-record.v1
2502.08346 v3

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:30:26.263938Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

  • verified exact11
  • verified fuzzy15
  • unresolved18
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41b5c26f-31bf-4a47-9493-14e8e4da620e · outbound

This paper cites Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations.

Graph Foundation Models for Recommendation: A Comprehensive Survey Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations

Reference 1

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local_arxiv, observed 2026-08-08T05:30:27.143039Z

Source-reported events for the cited work

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Observation fbbb1327-8bf8-4782-8cb2-272ad77154bf · outbound

This paper cites Leverage knowledge graph and large language model for law article recommendation: A case study of chinese criminal law.

Graph Foundation Models for Recommendation: A Comprehensive Survey Leverage knowledge graph and large language model for law article recommendation: A case study of chinese criminal law

Reference 4

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arxiv_id, observed 2026-08-08T05:30:27.107612Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 80c3232c-028e-4bd1-820b-79daa6e8627d · outbound

This paper cites Deep neural networks for youtube recom- mendations.

Graph Foundation Models for Recommendation: A Comprehensive Survey Deep neural networks for youtube recom- mendations

Reference 5

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raw_fallback, observed 2026-08-08T05:30:27.284843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation dee37c6f-f6be-44ac-bf7a-449a08667116 · outbound

This paper cites Towards graph foundation models for personalization.

Graph Foundation Models for Recommendation: A Comprehensive Survey Towards graph foundation models for personalization

Reference 7

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raw_fallback, observed 2026-08-08T05:30:27.277147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 54d9d350-c8bb-4444-940f-fdc64650942a · outbound

This paper cites Large Language Model with Graph Convolution for Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Large Language Model with Graph Convolution for Recommendation

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 82874af3-a9cd-43cc-9ba3-402885e3c95d · outbound

This paper cites A survey of graph neural networks for recommender systems: Challenges, methods, and directions.

Graph Foundation Models for Recommendation: A Comprehensive Survey A survey of graph neural networks for recommender systems: Challenges, methods, and directions

Reference 9

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raw_fallback, observed 2026-08-08T05:30:27.269558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 54076ae1-6ce5-4c93-b363-7fc71cf4c98e · outbound

This paper cites Integrating Large Language Models with Graphical Session-Based Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Integrating Large Language Models with Graphical Session-Based Recommendation

Reference 11

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Unavailable: canonical work link unavailable.

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Observation af0a09a4-24e8-48af-bc7d-0464ed3c8b6c · outbound

This paper cites Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models.

Graph Foundation Models for Recommendation: A Comprehensive Survey Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models

Reference 12

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Observation 337ee39b-85d3-4557-9a6a-6e3a8d1efc19 · outbound

This paper cites Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems.

Graph Foundation Models for Recommendation: A Comprehensive Survey Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems

Reference 13

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local_arxiv, observed 2026-08-08T05:30:26.903390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 1c9b48a2-a988-4e04-b532-68dbf234378e · outbound

This paper cites Hetgcot-rec: Heterogeneous graph-enhanced chain-of- thought llm reasoning for journal recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Hetgcot-rec: Heterogeneous graph-enhanced chain-of- thought llm reasoning for journal recommendation

Reference 14

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arxiv_id, observed 2026-08-08T05:30:26.888110Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e03ed27e-8a47-4c51-b334-bfe1e0c09afc · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for lan- guage understanding.

Graph Foundation Models for Recommendation: A Comprehensive Survey Bert: Pre-training of deep bidirectional transformers for lan- guage understanding

Reference 16

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raw_fallback, observed 2026-08-08T05:30:27.250786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e48b5388-1f51-4a3d-8f27-67f2664d9da5 · outbound

This paper cites Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.129078Z digest=sha256:982e7efbaad22309e12dd7681f7b6e0acf40cf78c2725ab9c796ca5836500d13

Observation b9f66913-ca59-4c03-aafc-173c7ee6e0ee · outbound

This paper cites Graph Foundation Models: Concepts, Opportunities and Challenges.

Graph Foundation Models for Recommendation: A Comprehensive Survey Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 103c9088-1dfa-47a1-a73f-fa808ad0ed39 · outbound

This paper cites Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations.

Graph Foundation Models for Recommendation: A Comprehensive Survey Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations

Reference 20

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.134519Z digest=sha256:100b6c1d0ea360501d00c2a4a6db95a2491c46833311e70aebecdc909f05c213

Observation be867918-cf10-45aa-859e-916881187403 · outbound

This paper cites XRec: Large Language Models for Explainable Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey XRec: Large Language Models for Explainable Recommendation

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.137458Z digest=sha256:e6636e80e8cbcd20dbcff941903322880a816463824e6541554bd986a33d23dd

Observation 9d3ac786-4a01-4af2-91e9-3a480b524247 · outbound

This paper cites LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation

Reference 22

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verified exact
local_arxiv, observed 2026-08-08T05:30:26.688357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation d42f537e-8d9a-43e9-9074-9288163fddba · outbound

This paper cites Denoising alignment with large language model for rec- ommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Denoising alignment with large language model for rec- ommendation

Reference 23

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raw_fallback, observed 2026-08-08T05:30:27.241021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2e80e4cd-f68b-4b6b-9cf1-0f150b6e2e55 · outbound

This paper cites Unveiling user preferences: A knowledge graph and llm- driven approach for conversational recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Unveiling user preferences: A knowledge graph and llm- driven approach for conversational recommendation

Reference 24

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Observation 9946735e-5520-46bf-862d-924466ac2cb5 · outbound

This paper cites Language models are unsupervised multitask learners.

Graph Foundation Models for Recommendation: A Comprehensive Survey Language models are unsupervised multitask learners

Reference 25

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raw_fallback, observed 2026-08-08T05:30:27.232144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ab2bf1d5-11b4-478b-849e-a4d0c8aba4c4 · outbound

This paper cites Representation learning with large language models for recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Representation learning with large language models for recommendation

Reference 26

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raw_fallback, observed 2026-08-08T05:30:27.222627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation d0d927b3-3d3d-472c-91a3-71fece0651b1 · outbound

This paper cites LKPNR: LLM and KG for Personalized News Recommendation Framework.

Graph Foundation Models for Recommendation: A Comprehensive Survey LKPNR: LLM and KG for Personalized News Recommendation Framework

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 54a191fc-47e1-47c7-b2af-aa8d77f7a92a · outbound

This paper cites LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation

Reference 28

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local_arxiv, observed 2026-08-08T05:30:26.376375Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.213508Z digest=sha256:a6b13574d6968622810f92e5ede18a267c80f33d9a3c63de10846c505a64198d

Observation f1b2cb71-759d-42eb-b082-c793e7737c47 · outbound

This paper cites An Automatic Graph Construction Framework based on Large Language Models for Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey An Automatic Graph Construction Framework based on Large Language Models for Recommendation

Reference 29

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local_arxiv, observed 2026-08-08T05:30:26.363460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.217331Z digest=sha256:7119415129a586722d1ec5eec0aa141c0ad5c7960a26bec1b81d0153b96e9a97

Observation 9f796cdc-2c67-4035-bf06-a5c041727b42 · outbound

This paper cites Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation

Reference 30

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Unavailable: canonical work link unavailable.

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Observation 4e609610-998d-4e46-8058-24c937c25766 · outbound

This paper cites Mgat: Multimodal graph attention network for rec- ommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Mgat: Multimodal graph attention network for rec- ommendation

Reference 31

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raw_fallback, observed 2026-08-08T05:30:27.212869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.223766Z digest=sha256:7c13b26ee1526af195992eba7b0356fb5adc59e010c87de1b664f8eff5e51d61

Observation 9a36c18f-48aa-4891-a1d5-7f7b7ef0e829 · outbound

This paper cites Enhancing Recommender Systems with Large Language Model Reasoning Graphs.

Graph Foundation Models for Recommendation: A Comprehensive Survey Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.229628Z digest=sha256:ae002a379439252431b28563b6ea13d38132a0b314c64e866da3ab357d646300

Observation 4e04747b-e61f-4e5a-bdb0-5b811caa1eb0 · outbound

This paper cites Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph.

Graph Foundation Models for Recommendation: A Comprehensive Survey Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.232535Z digest=sha256:cbe02ebc0feaef33c5b09db87bf650d5a604aa874e16fa6e2656944a023167c6

Observation 3537a0f0-ea30-4c6f-bb6a-8c7c86281386 · outbound

This paper cites LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations.

Graph Foundation Models for Recommendation: A Comprehensive Survey LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

Reference 35

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no resolver link, observed 2026-08-08T05:30:26.236314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.236314Z digest=sha256:133af8a6075b7b76f66480b3c98305b5700d3565f5edace48ac17ee5de7dd3a7

Observation acdafe76-cb5c-4716-bc57-cced593a1da5 · outbound

This paper cites Llmrec: Large language models with graph augmentation for recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey Llmrec: Large language models with graph augmentation for recommendation

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-08T05:30:27.193596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9a6d32e3-eb99-4961-90e0-56fc5919f9ef · outbound

This paper cites A comprehensive survey on graph neural networks.

Graph Foundation Models for Recommendation: A Comprehensive Survey A comprehensive survey on graph neural networks

Reference 37

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raw_fallback, observed 2026-08-08T05:30:27.183092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.243179Z digest=sha256:f59f3c3681c5ef9161659489aba234c8c8f8a3244d21baf15dee391afcfa6a5e

Observation 79e61bca-8ea7-48cb-b22d-364536187c67 · outbound

This paper cites Graph neural networks in recommender systems: a survey.

Graph Foundation Models for Recommendation: A Comprehensive Survey Graph neural networks in recommender systems: a survey

Reference 38

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raw_fallback, observed 2026-08-08T05:30:27.173672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.246416Z digest=sha256:65a3415789e197b84abde976fb795e7854bd0699e59a80678ffe8ab5fe76a835

Observation 84c37f65-f282-498c-ad68-6f31423bb104 · outbound

This paper cites PALR: Personalization Aware LLMs for Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey PALR: Personalization Aware LLMs for Recommendation

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.249126Z digest=sha256:d4946d7f85c8cc144e758560d9bd3074175eaa3a881f0c2a4325f8fdb5421362

Observation 1f15e471-79a9-471e-be07-a94d23580748 · outbound

This paper cites Ac- tions speak louder than words: Trillion-parameter sequen- tial transducers for generative recommendations.

Graph Foundation Models for Recommendation: A Comprehensive Survey Ac- tions speak louder than words: Trillion-parameter sequen- tial transducers for generative recommendations

Reference 40

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raw_fallback, observed 2026-08-08T05:30:27.162896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.251548Z digest=sha256:831f393d182024d3a94fdb41778d175dc74433c5b74aed68546d77f5fe999f91

Observation d20ca6a7-f08e-4f5b-8c4a-b93cb0f3e2ba · outbound

This paper cites Robust Recommender System: A Survey and Future Directions.

Graph Foundation Models for Recommendation: A Comprehensive Survey Robust Recommender System: A Survey and Future Directions

Reference 41

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local_arxiv, observed 2026-08-08T05:30:26.308773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.254409Z digest=sha256:3d9a3f732314ac3d0a798aec63e1e85f236f1cb96ef5a150104501a5d549a7a1

Observation da70e1ac-af2e-4fa7-a0e0-9ed929517088 · outbound

This paper cites Finerec: Exploring fine-grained sequential recommenda- tion.

Graph Foundation Models for Recommendation: A Comprehensive Survey Finerec: Exploring fine-grained sequential recommenda- tion

Reference 42

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raw_fallback, observed 2026-08-08T05:30:27.153077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.257400Z digest=sha256:148221e81f64d83893ea1c440a1d894b8468043b6c1f128551c6f425965cfa5b

Observation adcf5ee8-d515-48ea-ae6c-0d3dc2d4b6b9 · outbound

This paper cites A Survey of Large Language Models.

Graph Foundation Models for Recommendation: A Comprehensive Survey A Survey of Large Language Models

Reference 43

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unresolved
no resolver link, observed 2026-08-08T05:30:26.260203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.260203Z digest=sha256:ee176b9c702afe0a3c2b41f238aadc89a0bd511032dddd25e0c33d99d9e9f134

Observation 1f85efc6-76fc-45d7-a535-afdaa65bec5a · outbound

This paper cites DynLLM: When Large Language Models Meet Dynamic Graph Recommendation.

Graph Foundation Models for Recommendation: A Comprehensive Survey DynLLM: When Large Language Models Meet Dynamic Graph Recommendation

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-08T05:30:26.263938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.263938Z digest=sha256:eb82d39e2ba5125224b5a22dd9db04aad0777a00911a1a2ef706a7cd890f0965

Observation 246fe877-16cd-467d-ac54-83e631793a9c · outbound

This paper cites Comprehending Knowledge Graphs with Large Language Models for Recommender Systems.

Graph Foundation Models for Recommendation: A Comprehensive Survey Comprehending Knowledge Graphs with Large Language Models for Recommender Systems

Reference 2016

Resolution
verified exact
local_arxiv, observed 2026-08-08T05:30:26.954870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.094550Z digest=sha256:62a285883d52b89fe08a22309be8a93d5a61800f2a415e7c26d0f98c183af399

Observation f922a316-56ed-4bfa-ab02-5c2a157b9432 · outbound

This paper cites A Survey of Graph Meets Large Language Model: Progress and Future Directions.

Graph Foundation Models for Recommendation: A Comprehensive Survey A Survey of Graph Meets Large Language Model: Progress and Future Directions

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-08T05:30:26.126376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.126376Z digest=sha256:7d2c566c8f9e709cdd8c69d85d84cfa50b332d2cb5dd17abdc6561a604f62653

Observation 0e4ef08c-8666-4d6b-adcc-b3effcef3119 · outbound

This paper cites Graph learning based recommender systems: A review.

Graph Foundation Models for Recommendation: A Comprehensive Survey Graph learning based recommender systems: A review

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:30:27.203715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.226821Z digest=sha256:ede103fb00eff279808664cedc315e14ed8c8c1d18787569954bdf93162757e2

Observation 1caf232b-a618-4c65-b238-5a7f8d5fb6c7 · outbound

This paper cites A Prompting-Based Representation Learning Method for Recommendation with Large Language Models.

Graph Foundation Models for Recommendation: A Comprehensive Survey A Prompting-Based Representation Learning Method for Recommendation with Large Language Models

Reference 2021

Resolution
verified exact
local_arxiv, observed 2026-08-08T05:30:27.121387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.084240Z digest=sha256:2ae36b677e86369e2c696a74b1cef575f36f97e725e111c3429bbcc1d9514d9f

Observation 95a8cbae-1c66-4ea2-8cd7-3efbb68a818a · outbound

This paper cites Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning.

Graph Foundation Models for Recommendation: A Comprehensive Survey Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning

Reference 2023

Resolution
verified exact
local_arxiv, observed 2026-08-08T05:30:26.933559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.107046Z digest=sha256:e507aae6129428679d27b6637afc889140c1ffc068d6cade07a65a0ed758bd4b

Observation 68928be0-7fe1-4c99-8e61-7a6cafe679a7 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Graph Foundation Models for Recommendation: A Comprehensive Survey On the Opportunities and Risks of Foundation Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-08T05:30:26.080175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:30:26.080175Z digest=sha256:940d49bcae61dfba408af36cd3781b5659c9c0750d29f3c02f622e557c43e8ff

Observation de7931dc-748e-4b54-ac4a-022317fbc8c3 · outbound

This paper cites Large language models on graphs: A comprehensive survey.IEEE Transactions on Knowledge and Data Engineering ,.

Graph Foundation Models for Recommendation: A Comprehensive Survey Large language models on graphs: A comprehensive survey.IEEE Transactions on Knowledge and Data Engineering ,

Reference 2025

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:30:27.260651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:30:26.121902Z digest=sha256:df9b81762a961bab23e8251b3bc54b9e455387dc5d88a3ae10783ffc4d55b28e

Pith citing papers

No inbound Pith citation observations are available.