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Paper Citation Record · LEDGER

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems

As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2506.11421.

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

pith.paper-citation-record.v1
2506.11421 v3

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:13:33.819557Z

measured 36 of 36 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:46:41.592023Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T01:41:57.770488Z

Reference resolution

34 of 34 outbound references displayed

  • verified exact4
  • verified fuzzy2
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eee35393-336e-441d-ad76-0dede2709a76 · outbound

This paper cites A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system

Reference 1

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raw_fallback, observed 2026-08-07T04:13:35.981014Z

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.

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Observation 0bda57d4-eeae-407b-8408-66adfccb9741 · outbound

This paper cites Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning

Reference 2

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verified exact
local_arxiv, observed 2026-08-07T04:13:35.295119Z

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

source=pdf_text observed=2026-08-07T04:13:28.972981Z digest=sha256:1c4ab8cefa58d8cc8225991d8987f3906ddd4db6a06a677e131f05734ddb3690

Observation abdd9742-1c28-4747-9954-bd484892ab93 · outbound

This paper cites Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction

Reference 3

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source=pdf_text observed=2026-08-07T04:13:29.143067Z digest=sha256:2c66d307e49ad9de4d004a330d984220b15d89a0c7a79e9b2ea89bc7a5c5d05f

Observation f6538a0a-2cd0-4424-a360-57655d69f933 · outbound

This paper cites In the cloud, models use asynchronous microservices with Kubernetes/Kubeflow, supporting dynamic replica scaling.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems In the cloud, models use asynchronous microservices with Kubernetes/Kubeflow, supporting dynamic replica scaling

Reference 4

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raw_fallback, observed 2026-08-07T04:13:36.253962Z

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=2026-08-07T04:13:28.728361Z digest=sha256:9844e9d91ffe605297d0775cb5deb81aad6b794e5a1d4fe1d70c77ddfe856e4b

Observation d9235001-28bd-4695-9ab9-44423696ce6b · outbound

This paper cites Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model

Reference 5

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source=pdf_text observed=2026-08-07T04:13:29.256127Z digest=sha256:de4c9044f887187bf3cc87627b2b524089038b00ea089d049a3891601fd6600c

Observation 91042783-e73a-4d76-83d4-7c8a81ac1e8d · outbound

This paper cites Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks

Reference 6

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verified exact
local_arxiv, observed 2026-08-07T04:13:34.957779Z

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=2026-08-07T04:13:29.418664Z digest=sha256:a9e456d40ad9bd7244d4ad23632eabc4f9a4e78a3bdb925e4ba95f9a8aa51196

Observation 1251a9b9-cd73-4577-8dd3-6ed6c1cd4766 · outbound

This paper cites an unresolved cited work.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Unresolved cited work

Reference 7

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source=pdf_text observed=2026-08-07T04:13:29.585647Z digest=sha256:752b6b77b03f4db673cd08a7522985a21564e401b0cb40c07e3f653d6099aefc

Observation 72fd1bea-1882-4058-b85c-b80f5340ff28 · outbound

This paper cites A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field

Reference 8

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Observation 1bd91098-cfd6-4203-9fa7-211002bd3188 · outbound

This paper cites Analysis of collective response reveals that covid-19-related activities start from the end of 2019 in mainland china[J].

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Analysis of collective response reveals that covid-19-related activities start from the end of 2019 in mainland china[J]

Reference 9

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Observation 360f679b-19f6-48a4-959d-2508058b61d2 · outbound

This paper cites Multidimensional precipitation index prediction based on CNN-LSTM hybrid framework.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Multidimensional precipitation index prediction based on CNN-LSTM hybrid framework

Reference 10

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Observation e861c7eb-b35d-4ad3-9a55-bb3750af4071 · outbound

This paper cites CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules

Reference 11

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source=pdf_text observed=2026-08-07T04:13:30.215562Z digest=sha256:1c7dc168183de9fbc156dd5cbbb549c2bb251737c1ee07dc0d8dd71bd630fe0a

Observation 9073a282-9118-4f39-9c03-a1f506e7b705 · outbound

This paper cites Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture

Reference 12

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source=pdf_text observed=2026-08-07T04:13:30.315569Z digest=sha256:48e004e48a0752a4a5a1411d681a05356226527b880daed9a003cfc52e9be7bd

Observation a0c3d54d-2321-46c5-b06e-55bbcdccc25f · outbound

This paper cites CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction

Reference 13

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source=pdf_text observed=2026-08-07T04:13:30.495260Z digest=sha256:fc8c008ddb9ce55f40ce51a1aa692c6b8abcc879a5f5792c63fb569220cf7658

Observation 245889db-d492-4ec1-baa3-4fb94852f487 · outbound

This paper cites Construction and Analysis of Collaborative Educational Networks based on Student Concept Maps[J].

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Construction and Analysis of Collaborative Educational Networks based on Student Concept Maps[J]

Reference 14

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Observation bed74764-2d79-4bd3-924d-5fa6192b0cb0 · outbound

This paper cites Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning

Reference 15

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Observation f59ce348-5d2b-49ae-8c6c-a29e4129f329 · outbound

This paper cites Research on feature fusion and multimodal patent text based on graph attention network.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Research on feature fusion and multimodal patent text based on graph attention network

Reference 16

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Observation e566f856-0a11-45ef-b9cb-5a10b692de77 · outbound

This paper cites Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics

Reference 17

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source=pdf_text observed=2026-08-07T04:13:31.170338Z digest=sha256:3fa0f8ec453ea3df2567afcf6997c7b43a517aa66cf461e9c8d3b593013acbe2

Observation a52369ec-cd25-45bb-b6b4-c4077ed088be · outbound

This paper cites (2024, August).

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems (2024, August)

Reference 18

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source=pdf_text observed=2026-08-07T04:13:31.308223Z digest=sha256:937226bfc01a1381280783fd9c69642e1edc8479f6653979960d526387aa30b0

Observation 2af10960-6637-424d-a416-64dac88b66d0 · outbound

This paper cites Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy

Reference 19

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source=pdf_text observed=2026-08-07T04:13:31.498410Z digest=sha256:34feaa4854bcabbd1be8c5cc32337b239fb975837cc0584bf4fa818e8d97c822

Observation 6065c4a3-6aed-4992-a931-51edfa92e366 · outbound

This paper cites Enhanced Recommendation Combining Collaborative Filtering and Large Language Models.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Enhanced Recommendation Combining Collaborative Filtering and Large Language Models

Reference 20

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source=pdf_text observed=2026-08-07T04:13:31.668779Z digest=sha256:8a18c65a0983041904ad349ec962b032731277249ce4cd8c52406bb790ec5208

Observation 4ae7e3ff-8913-4ef7-bca3-6ffa80f85a9b · outbound

This paper cites Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing

Reference 21

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Observation cb564ff0-d608-4450-a9ba-c6e43267b6f6 · outbound

This paper cites Data Augmentation Through Random Style Replacement[J].

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Data Augmentation Through Random Style Replacement[J]

Reference 22

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source=pdf_text observed=2026-08-07T04:13:32.004187Z digest=sha256:201c66287031fddd01262a2ab87a9e7de6634e6c77b668435da0236c921530b0

Observation 63293af4-a9fb-4614-a408-c5569e9f1435 · outbound

This paper cites Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning

Reference 23

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local_arxiv, observed 2026-08-07T04:13:34.603726Z

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

source=pdf_text observed=2026-08-07T04:13:32.172622Z digest=sha256:50f29ff0bfa2711e3ef3c1902e0f32f5ceb5c76fae3812658906d97b261dbfd0

Observation 61fb725d-56ac-4b3f-b179-dfc530449db4 · outbound

This paper cites & Shi, T.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems & Shi, T

Reference 24

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Observation 65485eaa-7e81-4a04-a2d1-d6c67ebda126 · outbound

This paper cites an unresolved cited work.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Unresolved cited work

Reference 25

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Observation c70d313f-8436-42c9-a2fb-76bac2c39601 · outbound

This paper cites an unresolved cited work.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-07T04:13:32.607182Z digest=sha256:33c84c977d25f2f0f4c83b198a07ceef0a7d144612c3f2d09e23f447350d231b

Observation f18654b4-8143-48db-a879-14a9dae0a471 · outbound

This paper cites Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey

Reference 27

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source=pdf_text observed=2026-08-07T04:13:32.762880Z digest=sha256:837c6c1b9e9658d677491d0e55d4315f52e9091813b2770c6308f526eacfbc36

Observation 3e5c9024-c217-465c-ba4f-acabe6a3bc73 · outbound

This paper cites Generating Multimodal Images with GAN: Integrating Text, Image, and Style.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Generating Multimodal Images with GAN: Integrating Text, Image, and Style

Reference 28

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source=pdf_text observed=2026-08-07T04:13:32.886383Z digest=sha256:5e044af585d1a0bf108c193ee3f4c721a890d3cda27778d819725ad56f5d5b33

Observation 19d20e45-9204-4ca3-a3d7-37cdc25c9151 · outbound

This paper cites an unresolved cited work.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-07T04:13:33.015576Z digest=sha256:0bc2444a5e95580b5c58923614b43f5511dc51895ec2c0bb04e713c9be9bccd8

Observation 3eca97ea-1b97-4605-b251-5e23295e161f · outbound

This paper cites Automated Parking Trajectory Generation Using Deep Reinforcement Learning.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Automated Parking Trajectory Generation Using Deep Reinforcement Learning

Reference 30

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source=pdf_text observed=2026-08-07T04:13:33.187799Z digest=sha256:be0f2b925dbc7998cecb93c695ad62e8249b96dbec6d07d30b89e570f0b2034b

Observation aec500b5-627c-47bb-b2ef-ac20609afbae · outbound

This paper cites Optimized Path Planning for Logistics Robots Using Ant Colony Algorithm under Multiple Constraints.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Optimized Path Planning for Logistics Robots Using Ant Colony Algorithm under Multiple Constraints

Reference 31

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source=pdf_text observed=2026-08-07T04:13:33.339725Z digest=sha256:c03c26bec50b4e05de9badd61148a76e5765892567bede2128415cc87ac5665a

Observation b023e6fa-df59-45ec-a10b-4bad41e0c83a · outbound

This paper cites Research on Personalized Medical Intervention Strategy Generation System based on Group Relative Policy Optimization and Time-Series Data Fusion.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Research on Personalized Medical Intervention Strategy Generation System based on Group Relative Policy Optimization and Time-Series Data Fusion

Reference 34

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Observation 82a0161c-2cc0-460d-b8f3-6e6f97a502db · outbound

This paper cites Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising

Reference 35

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source=pdf_text observed=2026-08-07T04:13:33.620134Z digest=sha256:6caeff9f88e12ee1bd0fad5e87b5a04e23910d72d9773c1abf243f4adcf38a62

Observation 06a084de-add7-469d-8f13-b4bb40d9c499 · outbound

This paper cites Contextual Bandits for Unbounded Context Distributions.

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems Contextual Bandits for Unbounded Context Distributions

Reference 36

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verified exact
local_arxiv, observed 2026-08-07T04:13:34.072529Z

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=2026-08-07T04:13:33.819557Z digest=sha256:307e0ec941f451dba88ee53656bd0d9c57e790a0cfb899caf23436e2a2daed84

Pith citing papers

Observation ef03d108-f036-4e2e-b2df-f4cac056a8ff · inbound

SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation cites this paper.

SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems

Reference 32

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arxiv_id, observed 2026-05-19T01:41:57.773342Z

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.

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Observation 304e5980-4df1-4b05-8c38-56065e002e23 · inbound

Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users cites this paper.

Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems

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