Paper Citation Record · LEDGER

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.13112.

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

pith.paper-citation-record.v1
2305.13112 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:00:05.036643Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:09:48.844608Z

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 83b2b72e-75e2-4280-8e18-40df04c975a7 · inbound

CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models cites this paper.

CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T01:00:05.036643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5f40aa9-d4ea-4612-b554-e62160040c12 · inbound

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning cites this paper.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T20:29:42.353044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c384cd05-199e-45ed-b7f8-830af9528212 · inbound

A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms cites this paper.

A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T18:52:26.292247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43b18f6b-b4c0-4bd1-bf96-58f60edd7fd0 · inbound

Evaluating Recabilities of Foundation Models: A Multi-Domain, Multi-Dataset Benchmark cites this paper.

Evaluating Recabilities of Foundation Models: A Multi-Domain, Multi-Dataset Benchmark Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T14:24:24.104785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d77f9783-2dc2-4850-baaf-cc2c74673aaa · inbound

User Simulator-Guided Multi-Turn Preference Optimization for Reasoning LLM-based Conversational Recommendation cites this paper.

User Simulator-Guided Multi-Turn Preference Optimization for Reasoning LLM-based Conversational Recommendation Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:28:02.584217Z

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 d00edcdc-a0bd-4728-89db-834db473e053 · inbound

Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems cites this paper.

Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:09:48.845827Z

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.