Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.11372.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:08:31.274212Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T20:25:12.364159Z
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 2b461651-8f5b-407c-9dc3-ef4aa6fbf405 · inbound
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy cites this paper.
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T05:29:21.924311Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0cd98490-d9f9-4c45-8509-efeb93702f30 · inbound
Scaling New Frontiers: Insights into Large Recommendation Models cites this paper.
Scaling New Frontiers: Insights into Large Recommendation Models Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Reference 94
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T05:10:44.501387Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9aa5e2fc-26a3-4f20-9a73-f9abf14e8dac · inbound
Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model cites this paper.
Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:08:31.274212Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9be2457f-0cf5-45b5-8706-f93938e90c00 · inbound
DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction cites this paper.
DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:22:59.142867Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d2d9b67f-c441-4ee9-8e6e-2929d5b7ef77 · inbound
FuXi-\beta: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model cites this paper.
FuXi-\beta: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Reference 58
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T20:25:12.370392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.