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Paper Citation Record · LEDGER

Understanding Scaling Laws for Recommendation Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2208.08489.

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

pith.paper-citation-record.v1
2208.08489 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:11:41.470143Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

5
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c5de8e78-a1e6-4589-b1cb-6a5a8f0f1c3a · inbound

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders cites this paper.

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders Understanding Scaling Laws for Recommendation Models

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-24T03:05:57.013142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-24T03:03:51.053556Z digest=sha256:c4a5db54f46fd9d631541d74948d7262a7d2ae76257e7847d6569492b80fe4c1

Observation 80267f50-accd-47bc-82a6-2d8bc48f5cce · inbound

FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer cites this paper.

FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer Understanding Scaling Laws for Recommendation Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-09T10:11:41.470143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:11:41.470143Z digest=sha256:9079714c77252e4777fe687a193513939b1e156f3c3ff716a59b82e461e48e0c

Observation 0eaff254-29a4-4e20-af6d-1c10c879ed4c · inbound

Climber: Toward Efficient Scaling Laws for Large Recommendation Models cites this paper.

Climber: Toward Efficient Scaling Laws for Large Recommendation Models Understanding Scaling Laws for Recommendation Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T20:15:13.898263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T20:15:13.898263Z digest=sha256:8bcf45de6f253caa6c5a16a0b0fdb809971770de9ed3a74017777323edab1142

Observation 63f5e723-ff33-46d2-b7d7-9c89741a65e3 · inbound

Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval cites this paper.

Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval Understanding Scaling Laws for Recommendation Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T13:16:32.448740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:16:32.448740Z digest=sha256:ab42e77fbe570dc609476903fd46411ed5364391d94fca92442c22bb98256078

Observation 1cf080d7-0482-44e9-85d5-a35f581f68d9 · inbound

Scaling Transformers for Discriminative Recommendation via Generative Pretraining cites this paper.

Scaling Transformers for Discriminative Recommendation via Generative Pretraining Understanding Scaling Laws for Recommendation Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T11:02:28.271026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:02:28.271026Z digest=sha256:8ef4e31c6ec2017a97c1189117de9baf76756a9215a02a7c7542a06e87e7db5b

Observation e8ddbd45-87db-4413-8c1c-b36efb2acc1c · inbound

RankMixer: Scaling Up Ranking Models in Industrial Recommenders cites this paper.

RankMixer: Scaling Up Ranking Models in Industrial Recommenders Understanding Scaling Laws for Recommendation Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T15:35:48.319669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:35:48.319669Z digest=sha256:0f2d24ee8f91777d530d7865d3d2d921a8a0c16ae8af5a31ef3f2198792161eb

Observation 7dd2dbc1-3193-4ccf-964e-ca89ebf425f0 · 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 Understanding Scaling Laws for Recommendation Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T20:25:11.815668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:25:11.815668Z digest=sha256:ac04feaea8eb1c471e56d6b0b5d84708623b10cf4d63307a1f6c15a5bab678f2

Observation faa18328-6f12-4f42-be36-559a57149044 · inbound

Model Merging Scaling Laws in Large Language Models cites this paper.

Model Merging Scaling Laws in Large Language Models Understanding Scaling Laws for Recommendation Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:32:37.728406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T13:32:33.009367Z digest=sha256:5748f139aa6158762da6b71b638da544a63b2e9cb75d3e40959c43438d667782

Observation 2a2a5740-4f3a-4ca9-bc41-e70663c460f5 · inbound

SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs cites this paper.

SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs Understanding Scaling Laws for Recommendation Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-17T20:22:04.560240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T20:21:31.266176Z digest=sha256:ac8349e0ded41d2a6fc034efe5ffe19271f35c26b1e6685a644879af78ed8986

Observation 0282bc5f-1a1e-4bbc-9116-f1443ea30743 · inbound

Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation cites this paper.

Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation Understanding Scaling Laws for Recommendation Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-03T00:06:24.016376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:06:24.016376Z digest=sha256:52731a65372a56543003363f1e02013fdf8c1f76605e9f7a26a956b8655c7426

Observation b7ecbe37-0666-472d-b140-b23be1c1a593 · inbound

Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation cites this paper.

Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation Understanding Scaling Laws for Recommendation Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-10T14:30:30.294337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T14:25:53.909672Z digest=sha256:996208dbeedfc9afef4b5af029fd405e9c593f9227d8373b3aa3c2e5dcce5375

Observation 8791dda3-0097-4185-8eb5-a3895db2fa80 · inbound

RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems cites this paper.

RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems Understanding Scaling Laws for Recommendation Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:56:04.889366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T04:30:22.404616Z digest=sha256:d17aa1eaf37bcb90371a02b39d14aa89a3891d17b4c2fe31e5857bdc5b11a259

Observation 32bf1375-a8c1-4e0b-ad73-d3ac043a0a1e · inbound

RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems cites this paper.

RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems Understanding Scaling Laws for Recommendation Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:17:29.441108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T07:14:03.733251Z digest=sha256:276db0e3a911fa54d7a8753c06845c36a0258c963d45f5eb2487b9a5cd0fde60

Observation c714227a-3384-44d0-904e-a302cd28450c · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Understanding Scaling Laws for Recommendation Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:06:28.040117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T04:33:41.411292Z digest=sha256:6d27d7dab4354cd54681f4e8999fa4c07ef29eea00ca1efa7f9c17713be480ba

Observation 67b51849-b0ce-4d43-96db-e58afce6b6b4 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Understanding Scaling Laws for Recommendation Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-15T04:59:46.247134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T04:55:01.973832Z digest=sha256:6b0163d23815fc3750e97ed9bc3d33f180512227574ec560289805cd1e4970da

Observation 3a2aba71-4b66-406d-b134-4083bccad3e1 · inbound

Scaling Laws for Behavioral Foundation Models over User Event Sequences cites this paper.

Scaling Laws for Behavioral Foundation Models over User Event Sequences Understanding Scaling Laws for Recommendation Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:36:45.306367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T06:49:25.517510Z digest=sha256:e5d2bf41ef982672e275e1c8aaad29ac02a0d11c7de70eba1a594987f7424cf2

Observation b797cf67-154e-4a0c-9065-27a5000714a1 · inbound

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture cites this paper.

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture Understanding Scaling Laws for Recommendation Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-01T19:18:41.341694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:18:41.341694Z digest=sha256:8cecfed22c0a2a6e479d9eb94247e62dc8c66d580abb130d402113ffc794ad44

Observation f867e7eb-a5b7-49fc-a4be-fbea4383dba8 · inbound

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture cites this paper.

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture Understanding Scaling Laws for Recommendation Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-04T04:17:56.347904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:17:56.347904Z digest=sha256:4ad445bae63fdb6f57795a99af8c73cdda3dd8d0a82d8c853025e8279570f924

Observation 9440b6c3-714f-48ed-862a-313333e16ccc · inbound

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation cites this paper.

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation Understanding Scaling Laws for Recommendation Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-01T02:11:13.200065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T02:11:13.200065Z digest=sha256:dbe043de0e1f8f74ed80315f777e4c1a89f2011ff5330d005fce01726b287f62