Paper Citation Record · LEDGER

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 10 inbound Pith citation observations for arXiv:2412.00430.

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

pith.paper-citation-record.v1
2412.00430 v6

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:29:21.952820Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:08:30.799780Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:29:41.982741Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy6
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83efdca2-066d-4867-8724-533e6c9daa35 · outbound

This paper cites Understanding training efficiency of deep learning recommendation models at scale.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Understanding training efficiency of deep learning recommendation models at scale

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:29:22.784005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e5ae03e-1017-48fe-aa29-19d666e2ab31 · outbound

This paper cites Compressed interaction graph based framework for multi-behavior recommendation.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Compressed interaction graph based framework for multi-behavior recommendation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:29:22.752492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bb79713-6907-406e-a16d-aad1010273c0 · outbound

This paper cites URL https://doi.org/ 10.1145/3269206.3271761.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy URL https://doi.org/ 10.1145/3269206.3271761

Reference 8

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Observation 6ecdc9e9-8880-4bd1-b4a8-578e78de72e4 · outbound

This paper cites URL https://doi.org/ 10.1145/2911451.2911489.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy URL https://doi.org/ 10.1145/2911451.2911489

Reference 10

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Observation df6dc3c0-6fc3-4292-9807-d0659708c51f · outbound

This paper cites and McAuley, J.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy and McAuley, J

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:29:22.734919Z

Source-reported events for the cited work

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Observation b8682c34-4d12-4c69-8f90-4017372800ad · outbound

This paper cites Kaplan, J., McCandlish, S., Henighan, T., Brown, T.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Kaplan, J., McCandlish, S., Henighan, T., Brown, T

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.862695Z

Source-reported events for the cited work

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Observation b78a04fd-31a6-4855-8466-4156ef2f0ac3 · outbound

This paper cites Generalization through Memorization: Nearest Neighbor Language Models.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Generalization through Memorization: Nearest Neighbor Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.868527Z

Source-reported events for the cited work

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Observation c7026fbc-2788-4e8b-8937-76183caccd24 · outbound

This paper cites Deep double descent: Where bigger models and more data hurt.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Deep double descent: Where bigger models and more data hurt

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.884030Z

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Observation 8a44a1e5-8713-4a25-9544-de4538893053 · outbound

This paper cites Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.888575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4516905b-cd0e-4d24-ac9e-05e44f33d350 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.898288Z

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Unavailable: canonical work link unavailable.

Observation 356aa3f4-c1e6-41ea-9f35-35191e7ea9e6 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.919488Z

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Unavailable: canonical work link unavailable.

Observation 2b461651-8f5b-407c-9dc3-ef4aa6fbf405 · outbound

This paper cites Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.924311Z

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Observation 8ef23c92-98ab-4d57-8b4e-7d55a9007fdb · outbound

This paper cites URL https://doi.org/ 10.1145/3397271.3401142.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy URL https://doi.org/ 10.1145/3397271.3401142

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.929037Z

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Observation 3f77837a-adb7-4527-ab40-3e594257304e · outbound

This paper cites Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.942839Z

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Observation bc79fd9f-ea95-4cfe-900a-f49fcd7a5cf5 · outbound

This paper cites A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.947903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1886445-51db-4c26-8e9d-7edea1430ac1 · outbound

This paper cites X., and Wen, J.-R.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy X., and Wen, J.-R

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:29:22.691516Z

Source-reported events for the cited work

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Observation b5eb7000-f97d-42ba-884d-e56a09500104 · outbound

This paper cites K., Lee, J., Lundell, J., Kim, C., Kejariwal, A., and Owens, J.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy K., Lee, J., Lundell, J., Kim, C., Kejariwal, A., and Owens, J

Reference 1949

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:29:22.718517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d93663d8-64e4-4092-85d2-f8a354affd62 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.893450Z

Source-reported events for the cited work

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Observation a38f08f3-f56d-43c3-9f39-e85398b9fff0 · outbound

This paper cites Session-based Recommendations with Recurrent Neural Networks.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Session-based Recommendations with Recurrent Neural Networks

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T05:29:21.853563Z

Source-reported events for the cited work

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Observation 5119373b-a66f-4c2a-835a-7e897640670d · outbound

This paper cites Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation

Reference 2017

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Source-reported events for the cited work

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Observation 8dcdf218-6ca3-4ddc-8016-3e4b1ae9e564 · outbound

This paper cites Session-based Recommendation with Graph Neural Networks.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Session-based Recommendation with Graph Neural Networks

Reference 2018

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Source-reported events for the cited work

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Observation 16ed2d50-711b-41f2-a181-4f41b67f2f3b · outbound

This paper cites ISBN 9781450369763.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy ISBN 9781450369763

Reference 2019

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b7132c2-75c7-4c55-b510-2b159ca55404 · outbound

This paper cites Language models scale reliably with over-training and on downstream tasks.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Language models scale reliably with over-training and on downstream tasks

Reference 2020

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Observation e34354da-5d05-4ad4-ae06-0b534c0fc2cb · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias– 9 Submission and Formatting Instructions for ICML 2024 Table.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Reconciling modern machine-learning practice and the classical bias– 9 Submission and Formatting Instructions for ICML 2024 Table

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:29:22.768044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0702f5b4-50fd-4d81-b08e-1a6dc47727a3 · outbound

This paper cites Integrating large language models into recommendation via mutual aug- mentation and adaptive aggregation.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Integrating large language models into recommendation via mutual aug- mentation and adaptive aggregation

Reference 2022

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Observation 46279d6b-8a2c-4d35-93b0-a44b0a7f190e · outbound

This paper cites Understanding Emergent Abilities of Language Models from the Loss Perspective.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy Understanding Emergent Abilities of Language Models from the Loss Perspective

Reference 2023

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Observation 194546f0-11f1-4a79-948c-f96f20c99d3f · outbound

This paper cites MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation.

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation

Reference 2024

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Pith citing papers

Observation 8f7dd588-174c-449d-adf8-5a38bc4ec9dc · inbound

Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens cites this paper.

Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 35

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Observation c2a1f7cc-3f82-481e-a66a-c625f28a61d1 · inbound

TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation cites this paper.

TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 44

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Observation f781cc6a-32a8-4abe-ba9d-1cb0de242e4c · inbound

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

FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 47

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Observation 0b4ba92c-656d-4616-86e8-62a14992ac7c · inbound

Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model cites this paper.

Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:30.799780Z

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Observation b2ca6fd4-82d1-4379-88a2-bafea2e8f5d5 · inbound

DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction cites this paper.

DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 42

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Observation 9485fd89-4f0d-4cf6-9128-78d3b8fe64f8 · 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 Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 48

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Observation 120cf197-ba00-4293-a067-7d23b3f8a7a9 · inbound

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling cites this paper.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 25

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Observation e4f6957a-a75f-4ac2-ac98-c0505b3cb6d4 · inbound

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment cites this paper.

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 65

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Observation 2aec0546-7f1c-47b0-90d2-ebb4f5042254 · inbound

IE as Cache: Information Extraction Enhanced Agentic Reasoning cites this paper.

IE as Cache: Information Extraction Enhanced Agentic Reasoning Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-10T10:49:55.775516Z

Source-reported events for the cited work

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Observation 198060f0-6c71-42e6-85c4-a645cd4ac22c · inbound

The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders cites this paper.

The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:29:41.984308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.