Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.01944.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:22:13.434168Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T23:04:16.530388Z
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 76bea16f-9905-4258-bc65-2b3e005d6b4e · inbound
Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering cites this paper.
Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:22:13.434168Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd43cf8b-bfe9-49c4-81b4-b86ec74dbe43 · inbound
CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems cites this paper.
CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T23:04:16.545799Z
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.