Paper Citation Record · LEDGER

Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2401.04997.

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

pith.paper-citation-record.v1
2401.04997 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:53.890011Z

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

7
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 0bcb839f-441e-43e9-8410-effe79021641 · 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 Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:53.890011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39731655-2146-4b39-bf66-9145fc2999d8 · inbound

Demystifying ChatGPT: How It Masters Genre Recognition cites this paper.

Demystifying ChatGPT: How It Masters Genre Recognition Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:13.893279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56647cbf-57df-41b1-a7af-fff901ccc638 · inbound

Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation cites this paper.

Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T15:27:10.377854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50596315-5431-41d2-82a2-63f683c57026 · inbound

Biases in LLM-Generated Musical Taste Profiles for Recommendation cites this paper.

Biases in LLM-Generated Musical Taste Profiles for Recommendation Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T15:09:52.832881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bccc5a93-124d-4fc2-b5da-b30b86a79c7c · inbound

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems cites this paper.

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:28.371504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f41cf86-6442-49b4-abe0-7e5a15b28783 · inbound

GraphRAG-IRL: Personalized Recommendation with Graph-Grounded Inverse Reinforcement Learning and LLM Re-ranking cites this paper.

GraphRAG-IRL: Personalized Recommendation with Graph-Grounded Inverse Reinforcement Learning and LLM Re-ranking Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:27:36.572843Z

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 f821e030-b835-4c35-8b12-6d0cbe7a3eab · inbound

From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents cites this paper.

From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:29:47.592995Z

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 b080c096-2a58-42ad-8f96-335dc343b66c · inbound

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees cites this paper.

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:31:08.064197Z

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 224b624c-a191-47eb-9908-10d178089129 · inbound

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees cites this paper.

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:41:17.628323Z

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 df71b9cc-71e7-4982-8c55-1380e235bd94 · inbound

Tokenizing Numerical and Embedding Features for LLM RecSys cites this paper.

Tokenizing Numerical and Embedding Features for LLM RecSys Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T01:00:39.401569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33c7310f-06c8-46a0-898d-21973b7b399a · inbound

Tokenizing Numerical and Embedding Features for LLM RecSys cites this paper.

Tokenizing Numerical and Embedding Features for LLM RecSys Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T07:26:30.527757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 118793f2-99da-4f19-b1e9-abb6f1862224 · inbound

Tokenizing Numerical and Embedding Features for LLM RecSys cites this paper.

Tokenizing Numerical and Embedding Features for LLM RecSys Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T01:43:22.490172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.