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Paper Citation Record · LEDGER

Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2311.05884.

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

pith.paper-citation-record.v1
2311.05884 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:35:48.349006Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T16:54:59.135304Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
  • unresolved0
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  • malformed identifier0
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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 d0fee91a-e9b3-43ea-be34-5dfb00e1f6ef · inbound

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

RankMixer: Scaling Up Ranking Models in Industrial Recommenders Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T15:35:48.349006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:35:48.349006Z digest=sha256:ab73b07b56b895d1deec28d205f14f29d997be28a7a78457c4b3b9c425970f9a

Observation 95fb2a6f-44d4-4d4b-ab52-e0f3fd604928 · inbound

From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction cites this paper.

From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-03T22:08:54.962723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:08:54.962723Z digest=sha256:3d6fe154c808192df0af643022661498dce148732e7882a3fc019045482c6912

Observation ce41e478-714a-4f5a-b436-b69824ddb0ee · 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 Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-25T06:55:26.127154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T06:52:50.606682Z digest=sha256:e38b5487087075bf944a44a02eccf9616de585e1580e8f24f9a070e609df5867

Observation f7b968dd-9391-4039-948d-856e2455f494 · inbound

IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems cites this paper.

IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:01:01.301285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T17:20:50.041889Z digest=sha256:a2d8cd62531d1c94abe6aefd6a7d923b6ac7632637aed644ed0660998924fc01

Observation f331e419-9fd1-4ca5-a36c-2b53837a7827 · inbound

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds cites this paper.

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-10T12:50:25.125727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T12:47:50.618022Z digest=sha256:6fc3615719c8fbf7db10760fa65d795ba0f71baa9d1ec5b26d6f6492372db3f5

Observation 7182b9f8-9e41-4da2-bf82-e453b64a4274 · 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 Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:56:04.761352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T04:30:22.404616Z digest=sha256:ffb75d7c5709c68845d354ae23328f49c1e449149995d6c1f5d9fbc1e8776eb0

Observation 27943f23-ea4f-4b7e-817b-d423afcfe84b · 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 Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:17:29.434971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T07:14:03.733251Z digest=sha256:6b43e2604308979055f4e5107e5b12a179e02ea2a3165a3ec1eebd70798ffaea

Observation 2c930dc5-6381-47fc-a95e-0e74ca6aec39 · inbound

LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction cites this paper.

LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:26:04.224776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T01:44:44.346732Z digest=sha256:dbfd7cf6c51b17faea76cb8ceafaa1f093664faf0c3b1c308d2f3bf8ccc27741

Observation 39ffe000-5d56-41dd-bba7-9948530c4013 · 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 Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-22T08:21:16.735678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T08:21:06.959344Z digest=sha256:c7833e66fa9b739ce9ab606ec589efb9f090d659a8cb27463f467d6289787c08

Observation 867e3bdf-a028-4911-bd6c-009fd534120e · 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 Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-06-30T16:54:59.137882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T16:45:43.932116Z digest=sha256:a55e8ec7d20be32fbe004b4aab5c5c10c3b2ac6c66e5398abdc35b02128e6ccd

Observation d2b380ab-7400-4901-bb44-a492f3c351d6 · inbound

TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search cites this paper.

TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-02T05:22:03.924701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:22:03.924701Z digest=sha256:6be7683264979334ae4158bb6a94ee0d11fdc329ed1e97b34222ad3504e5e941

Observation 4637f6f6-aadc-4cce-b7b9-5a8974239095 · 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 Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-01T19:18:42.423542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:18:42.423542Z digest=sha256:6ddda5cffac81e764e5e1a4252e1421d2b867782845164253fd0e86942d5a371

Observation 7ad71112-ff63-4f0b-abd4-68f2136e39ac · 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 Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-04T04:17:56.594424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:17:56.594424Z digest=sha256:59ba60291deb82298cde1980652d0dd098a9ce891e14da89cb571f9c474b8c48

Observation b907e643-ac41-41fa-b9b8-d951f41abce3 · inbound

UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction cites this paper.

UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-01T11:07:55.942562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T11:07:55.942562Z digest=sha256:e388a9d2151d30a86910a0f8c9df5e2fbbaf26c6ccae89dc17e3b1c8601c0899

Observation bf90326d-fd8d-4d6c-b78f-32efa47d2124 · inbound

LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation cites this paper.

LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-01T09:09:36.168554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T09:09:36.168554Z digest=sha256:909cf4bcd6a3baf6f0d704cab328ff5ab75e4565de8d99d99ab121a66b69c239

Observation 50f6916b-a937-48a1-8bee-203d867aaae7 · inbound

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation cites this paper.

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 28

Resolution
unresolved
no resolver link, observed 2026-07-31T23:20:19.268498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T23:20:19.268498Z digest=sha256:8d22e5c0a8e0331849bf81e5531f3fe9ca5f9680a14d8c0cedf637e15facc76d

Observation ac60bb36-fc01-44a4-ba1e-b3a171e124fa · inbound

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation cites this paper.

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-04T04:02:20.028664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:02:20.028664Z digest=sha256:96812630a7960f96be3b05498ac59ab916271e83a819420f386aae5647b0dbda

Observation 9b337c37-1e4c-4ea3-81bf-7f6b92f0c334 · inbound

TransX: Scaling Transformer-based Recommendation via Behavioral and Serving Stream Crossings cites this paper.

TransX: Scaling Transformer-based Recommendation via Behavioral and Serving Stream Crossings Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-03T16:52:53.224823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:52:53.224823Z digest=sha256:227e1cffb28947feb3c53f6738151826af95b0cfaa235886cd6c082008ae4047