Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T10:20:15.456852Z
Paper Citation Record · LEDGER
RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems
As of 10 August 2026, this Paper Citation Record lists 1 of 1 outbound references and 3 inbound Pith citation observations for arXiv:2508.00201.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T05:30:31.262162Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T23:49:15.091126Z
Reference resolution
1 of 1 outbound references displayed
- verified exact1
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c1233483-a8ac-4cc1-91c8-99122f37e238 · outbound
This paper cites Quantum-Corrected Thermodynamics of Conformal Weyl Gravity Black Holes: GUP Effects and Phase Transitions.
RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems Quantum-Corrected Thermodynamics of Conformal Weyl Gravity Black Holes: GUP Effects and Phase Transitions
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T10:20:15.488869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
Observation d2dbd2c8-c9ec-4aa8-b86d-246a475c313c · inbound
Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest cites this paper.
Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:30:31.262162Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f82ddef1-d358-4f00-868a-3897120a8725 · inbound
Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback cites this paper.
Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T23:50:04.897554Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c5ce0186-fcc4-4ad2-8811-30ac01f597b1 · inbound
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems cites this paper.
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T23:49:15.093689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.