Paper Citation Record · LEDGER

Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2305.06474.

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

pith.paper-citation-record.v1
2305.06474 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:39:50.980458Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

24
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f88682f8-f72e-4fe8-8c0f-bdb9a514238b · inbound

Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals cites this paper.

Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:39:50.980458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e16ad4aa-4cdc-40aa-bec8-6ff444f21c7a · inbound

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders cites this paper.

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:21.993781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc5629c1-20ed-435b-8a1d-f86d4178d722 · inbound

Fair Document Valuation in LLM Summaries via Shapley Values cites this paper.

Fair Document Valuation in LLM Summaries via Shapley Values Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:08.430620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b1169db-cf39-4bff-9ce0-1ce26aa3a74b · inbound

GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems cites this paper.

GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:56:21.775273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f92d5c02-792c-4f55-b0a3-1c9ba68fc5e6 · inbound

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models cites this paper.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:53.796277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41a9e943-acce-4184-baee-49a45540d343 · inbound

KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation cites this paper.

KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T19:23:49.205988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7dab85d-3d4c-4562-b1d4-75405209d433 · inbound

CLAImate: AI-Enabled Climate Change Communication through Personalized and Localized Narrative Visualizations cites this paper.

CLAImate: AI-Enabled Climate Change Communication through Personalized and Localized Narrative Visualizations Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:09:12.354843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 889eaca3-2711-4bfb-82db-442df65768c1 · inbound

Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model cites this paper.

Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T13:53:55.995108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b5c305f-85f3-488f-adda-02849442b081 · inbound

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing cites this paper.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T20:49:37.776970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42bf91f2-6998-429d-9d62-adae884193c2 · inbound

Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors cites this paper.

Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T19:08:29.124758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e45e0da-4d32-46c1-b722-9152f1e51962 · inbound

A Survey on Generative Recommendation: Data, Model, and Tasks cites this paper.

A Survey on Generative Recommendation: Data, Model, and Tasks Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 68

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T03:50:52.142854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2cc4cee-d8a0-4386-ab3e-437d61333916 · inbound

VENOMREC: Cross-Modal Interactive Poisoning for Targeted Promotion in Multimodal LLM Recommender Systems cites this paper.

VENOMREC: Cross-Modal Interactive Poisoning for Targeted Promotion in Multimodal LLM Recommender Systems Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T04:00:16.185234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 057f7096-78dc-4a6a-ad64-75ce84a49f23 · inbound

TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation cites this paper.

TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T16:50:36.338865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa4a47f7-0cca-4948-9aa3-ed0917570ffe · inbound

Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation cites this paper.

Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T08:10:09.911104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dfaa26c-0cb2-44f7-87e9-f51ec1405b1a · inbound

A Reproducibility Analysis of PO4ISR: Diagnosing and Mitigating Semantic Drift in LLM-Based Session Recommendation cites this paper.

A Reproducibility Analysis of PO4ISR: Diagnosing and Mitigating Semantic Drift in LLM-Based Session Recommendation Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:49:19.527964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c6ad1261-1c4e-456b-a616-a4b674e55ffd · inbound

CoPersona: Collaborative Persona Graphs for Robust LLM Personalization cites this paper.

CoPersona: Collaborative Persona Graphs for Robust LLM Personalization Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-03T18:28:48.380125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92ad5461-517c-4555-83c5-8d7349e414ba · inbound

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models cites this paper.

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T12:15:44.818571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.