Pith. sign in

Paper Citation Record · LEDGER

RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.00724.

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

pith.paper-citation-record.v1
2109.00724 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:27:31.660233Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T19:33:25.993202Z

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 7d7e7552-7b71-4c48-9841-f03e6a8dd0e3 · inbound

CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction cites this paper.

CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-15T20:27:31.660233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:27:31.660233Z digest=sha256:c40e1348814a204b120d48ba037c9960ea2185b710f417532aa0c56b96998956

Observation 3cee789c-6b94-4700-92f8-a00e65d07ef2 · inbound

RadarSeq: A Temporal Vision Framework for User Churn Prediction via Radar Chart Sequences cites this paper.

RadarSeq: A Temporal Vision Framework for User Churn Prediction via Radar Chart Sequences RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce

Reference 92

Resolution
metadata mismatch
local_arxiv, observed 2026-08-15T19:33:26.116711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T19:33:25.614393Z digest=sha256:5eeef8662635546fad653c1fb279ad75caeed0c50effbde67e72b64d557df418