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Paper Citation Record · LEDGER

Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1905.01997.

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

pith.paper-citation-record.v1
1905.01997 v3

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T06:02:36.224751Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T10:07:33.914496Z

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 08f783a2-05b7-4937-bb84-e1518450bea5 · inbound

Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models cites this paper.

Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T06:02:36.224751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T06:02:36.224751Z digest=sha256:8734cba3fc89e580eebc2fda88412deb650860f0531fd1f984e21d2eb49acd7b

Observation ab6c2104-cb76-41e9-9a9b-3ff6f724376a · inbound

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors cites this paper.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-10T10:07:33.917533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T10:07:33.813712Z digest=sha256:0bfe2eb705798a06c0a75999abd93ad2939a1d927699578d24fb6c37521f9bcd