Paper Citation Record · LEDGER

Leveraging Large Language Models for Pre-trained Recommender Systems

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2308.10837.

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

pith.paper-citation-record.v1
2308.10837 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:03:40.825462Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T21:06:50.722455Z

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 90a70fcc-2078-46d8-8621-1ff9294591eb · inbound

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models cites this paper.

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:03:17.111574Z

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.

Observation 08ae441f-030c-4e19-b7ca-182ca0221df9 · inbound

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs cites this paper.

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 240

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T15:51:29.482869Z

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.

Observation 0f9f4c89-c78f-4f88-ab98-165ecc8b2ed6 · inbound

Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation cites this paper.

Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:40.825462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ebfb13f-9e03-4ab4-944e-edf9885d600e · inbound

Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model cites this paper.

Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T13:53:57.113945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cfdaa3c-145e-4f2d-9bd8-baaf783018aa · inbound

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation cites this paper.

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-18T21:06:50.725426Z

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.

Observation 698e79bd-2505-4d3c-af7c-cce126b45aa2 · inbound

A Survey on Generative Recommendation: Data, Model, and Tasks cites this paper.

A Survey on Generative Recommendation: Data, Model, and Tasks Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:50:52.068473Z

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.

Observation 33894f8c-a1b6-489f-b6b4-30fe16403224 · inbound

TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoning cites this paper.

TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoning Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:42:03.682286Z

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.

Observation 35e78f04-eb6a-4fda-beed-eb42ba79ac1f · inbound

Topology-Aware Tokenization for Generative Recommendation cites this paper.

Topology-Aware Tokenization for Generative Recommendation Leveraging Large Language Models for Pre-trained Recommender Systems

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T14:59:16.983921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.