Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Learning Recommendation Model for Personalization and Recommendation Systems
As of 23 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 42 inbound Pith citation observations for arXiv:1906.00091.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-20T06:30:07.809122+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-14T07:32:10.979183Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T06:15:00.866473Z
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 a52361d6-0447-4396-976a-468ceead7c8f · inbound
TrainMover: An Interruption-Resilient Runtime for ML Training cites this paper.
TrainMover: An Interruption-Resilient Runtime for ML Training Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T07:15:28.684625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 66764df5-f69b-4386-8f0f-556e96caedce · inbound
Learning from Natural Language Feedback for Personalized Question Answering cites this paper.
Learning from Natural Language Feedback for Personalized Question Answering Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T22:56:52.962848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 80a6d7fd-78a6-48a8-a44b-c354c62a2da7 · inbound
Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendation cites this paper.
Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendation Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T23:30:29.329487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 0670eedb-345a-4939-9d0e-a9a200514115 · inbound
Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendation cites this paper.
Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendation Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T19:14:19.501778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 437ebc72-f1ac-4813-8fe2-cab3ac5ef00e · 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 Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T20:22:04.539363Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation aa9855ad-00bd-4f0b-ba69-139ac20aa2ef · inbound
LayerPipe2: Multistage Pipelining and Weight Recompute via Improved Exponential Moving Average for Training Neural Networks cites this paper.
LayerPipe2: Multistage Pipelining and Weight Recompute via Improved Exponential Moving Average for Training Neural Networks Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T00:38:44.811193Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 156b4117-d410-44e5-97ca-32970b26ed25 · inbound
Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking cites this paper.
Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T06:55:26.148167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 713422fe-11eb-4ad1-a67c-87e9c45badfa · inbound
Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation cites this paper.
Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T17:08:01.116934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 732c80b1-dc99-4311-b2c9-a7c8e6843612 · inbound
Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation cites this paper.
Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:41:04.295305Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation f5245199-fbb2-47f2-a441-283d8a87c8a3 · inbound
SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling cites this paper.
SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:11:03.319638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 85fe608b-7336-4baa-943b-2c1ae5c6d12c · inbound
Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading cites this paper.
Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 177
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T06:21:26.879419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 92801a6a-92fc-4050-b7c8-50b95d2f853f · inbound
RecFlash: Fast Recommendation System on In-Storage Computing with Frequency-Based Data Mapping cites this paper.
RecFlash: Fast Recommendation System on In-Storage Computing with Frequency-Based Data Mapping Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T00:46:12.643727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 123b6543-c32f-4b6c-ab92-234559e4b00e · inbound
Sparse-on-Dense: Area and Energy-Efficient Computing of Sparse Neural Networks on Dense Matrix Multiplication Accelerators cites this paper.
Sparse-on-Dense: Area and Energy-Efficient Computing of Sparse Neural Networks on Dense Matrix Multiplication Accelerators Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T09:11:27.022071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 8349cd71-8a94-499d-8e3d-f2f9c6ffa5e3 · inbound
Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale cites this paper.
Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:41:52.445756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 64b10e3d-1070-4688-acee-f6ad6cca5aff · inbound
SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data cites this paper.
SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:16:09.056466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation cfdbbdf0-54e8-499b-a450-25f986786ab9 · inbound
Recommender Systems as Control Systems cites this paper.
Recommender Systems as Control Systems Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:16:09.118865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation dfe57c25-e9a0-4927-abbf-3ef5511f7180 · inbound
One Pool, Two Caches: Adaptive HBM Partitioning for Accelerating Generative Recommender Serving cites this paper.
One Pool, Two Caches: Adaptive HBM Partitioning for Accelerating Generative Recommender Serving Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-08T19:49:07.388210Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 0aada665-05ba-4e51-aab1-eafdcf6406bf · inbound
TENNOR: Trustworthy Execution for Neural Networks through Obliviousness and Retrievals cites this paper.
TENNOR: Trustworthy Execution for Neural Networks through Obliviousness and Retrievals Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 89
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:00:55.874329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 56ff1d2f-bb37-42bd-92d1-1f4ba2d5213f · inbound
A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems cites this paper.
A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T03:11:19.365803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 71559fa9-c4c8-4794-84d9-4f50955cc143 · inbound
LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries cites this paper.
LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T02:41:17.773871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 6176435c-1e13-486d-afeb-48272905fe06 · inbound
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:06:28.182969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 999e0d88-e30a-4e54-a82c-b80bcc0cd472 · inbound
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 57
- Resolution
- verified exact
- local_arxiv, observed 2026-05-15T04:59:46.107966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 789fe317-69e6-4a98-b6d4-9fdb99aa4c47 · inbound
MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces cites this paper.
MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 82
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T01:47:05.113229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 950e7581-4723-4dc6-b07b-07e8de0edaf7 · inbound
MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces cites this paper.
MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 82
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-20T22:19:07.550773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 61608bae-9665-4e09-b9eb-df901efc53a8 · inbound
Agentic Recommender System with Hierarchical Belief-State Memory cites this paper.
Agentic Recommender System with Hierarchical Belief-State Memory Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 14
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-15T02:23:31.961718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation f5a8fdb8-ae59-4b09-a295-187ea4a02541 · inbound
Agentic Recommender System with Hierarchical Belief-State Memory cites this paper.
Agentic Recommender System with Hierarchical Belief-State Memory Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 14
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-19T13:32:19.354227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation ffb0502b-5e99-46a9-8857-34ea40e8a48b · inbound
FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation cites this paper.
FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T08:21:16.717083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 6b715bb1-a19e-4908-9d95-56aba81824d4 · inbound
FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation cites this paper.
FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T16:54:59.116369Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation fda27063-da7a-40fe-9d98-bd2afc3e0825 · inbound
LLM Retrieval for Stable and Predictable Ad Recommendations cites this paper.
LLM Retrieval for Stable and Predictable Ad Recommendations Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T04:51:05.893493Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 29de9ae8-297e-4f84-af5e-08f741a6dd9d · inbound
LENS: A Staged Design for Interaction Granularity in Sequential CTR Prediction cites this paper.
LENS: A Staged Design for Interaction Granularity in Sequential CTR Prediction Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T20:43:58.380081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation bd2f0cbe-b64d-4590-8edc-657d1b9e180a · inbound
Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems cites this paper.
Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T00:04:05.165305Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation ec9e7af8-7c94-4f43-a72d-af121bb83958 · inbound
Fine-Tuned LLM as a Complementary Predictor Improving Ads System cites this paper.
Fine-Tuned LLM as a Complementary Predictor Improving Ads System Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T13:23:28.693470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 852b672c-9eca-4034-a125-98bde4183e77 · inbound
On the Practice of Scaling Search Conversion Rate Prediction cites this paper.
On the Practice of Scaling Search Conversion Rate Prediction Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T15:03:31.988945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation a6de5ebb-993d-4f9c-8939-cbe844c9d329 · inbound
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems cites this paper.
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 40
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-28T20:42:37.297892Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 82383789-182f-4873-9c83-3d85a6ce31f6 · inbound
Private Embedding Lookup with Encrypted Compact Queries under Fully Homomorphic Encryption cites this paper.
Private Embedding Lookup with Encrypted Compact Queries under Fully Homomorphic Encryption Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 51
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T03:36:29.733783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 1962b381-c635-47a3-9bbd-70cd6e5b6aca · inbound
SSRLive: Live Streaming Recommendation with Dynamic Semantic ID cites this paper.
SSRLive: Live Streaming Recommendation with Dynamic Semantic ID Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T20:07:21.840712Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation e3210d88-7dc7-4e75-8117-ed7ae76cc4ab · inbound
Recommendation as Generation: Unifying Personalized Video Generation and Recommendation at Industrial Scale cites this paper.
Recommendation as Generation: Unifying Personalized Video Generation and Recommendation at Industrial Scale Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T20:20:07.236698Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation 08441457-6189-4746-972e-d78c859e790e · inbound
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework cites this paper.
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T05:54:43.478745Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d7c6881f-d8b4-4699-a4a8-fdad5975b912 · inbound
UniSGR: Unified Framework for Semantic ID Generation and Ranking cites this paper.
UniSGR: Unified Framework for Semantic ID Generation and Ranking Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T21:55:00.514210Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 49394953-ed2b-41a3-a2a4-3354e2a7012e · inbound
From Raw IDs to Semantic Planning: How Recommender Systems Utilize Information at Scale cites this paper.
From Raw IDs to Semantic Planning: How Recommender Systems Utilize Information at Scale Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T02:17:16.415801Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation da9c2bcb-0894-4edb-b539-059c958e272c · inbound
Tokenizing Numerical and Embedding Features for LLM RecSys cites this paper.
Tokenizing Numerical and Embedding Features for LLM RecSys Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T01:00:39.401569Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2784517-29b3-4835-9f14-0368d463215b · inbound
MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Search cites this paper.
MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Search Deep Learning Recommendation Model for Personalization and Recommendation Systems
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T07:32:10.979183Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.