Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.16738.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:25:14.365955Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T18:42:29.170551Z
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 f431f06f-adf8-4ba3-90f0-d419f91bbdbc · inbound
The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges cites this paper.
The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
Reference 72
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:25:14.365955Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3aa92a5e-cc1a-4e7d-bcae-9726396d88f0 · inbound
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges cites this paper.
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
Reference 248
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T18:42:29.171946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.