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Paper Citation Record · LEDGER

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search

As of 9 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.01387.

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

pith.paper-citation-record.v1
2607.01387 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-03T18:34:22.616679Z

measured 50 of 50 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d596d7a0-9de2-4831-8677-0738d0820512 · outbound

This paper cites an unresolved cited work.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Unresolved cited work

Reference 1

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Observation 602c0a44-13ed-40c4-95df-9a5e48feddcf · outbound

This paper cites Towards high-order complementary recommendation via logical reasoning network,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Towards high-order complementary recommendation via logical reasoning network,

Reference 2

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Observation 18a2b224-709f-4015-aa11-f502eeabf6df · outbound

This paper cites Measuring recommendation explanation quality: The conflicting goals of explanations,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Measuring recommendation explanation quality: The conflicting goals of explanations,

Reference 3

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Observation 05ff7143-51a1-424c-bbff-dee71a528e4b · outbound

This paper cites Ex3: Explainable attribute-aware item-set recommendations,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Ex3: Explainable attribute-aware item-set recommendations,

Reference 4

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Observation 1e35c35d-8867-45bb-8d3b-1080ac8beb09 · outbound

This paper cites Collaborative filtering recommender systems,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Collaborative filtering recommender systems,

Reference 5

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Observation e3832ffe-f716-4dbb-a8d7-73f322aa9260 · outbound

This paper cites Matrix factorization techniques for recommender systems.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Matrix factorization techniques for recommender systems

Reference 6

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Observation 1f22def9-6cd6-4976-bf00-fc1639ac77e4 · outbound

This paper cites Asymmetrical hierarchical networks with attentive interactions for interpretable review-based recommendation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Asymmetrical hierarchical networks with attentive interactions for interpretable review-based recommendation,

Reference 7

Resolution
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Observation 80403eec-3d32-4eb9-b2cb-7babaaf25a71 · outbound

This paper cites Explainable recommendations via attentive multi-persona collaborative filtering,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Explainable recommendations via attentive multi-persona collaborative filtering,

Reference 8

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Observation 6c92415c-3706-4f04-b519-e14616b8474e · outbound

This paper cites Attention-guide walk model in heterogeneous information network for multi-style recommendation explanation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Attention-guide walk model in heterogeneous information network for multi-style recommendation explanation,

Reference 9

Resolution
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Observation c8129b33-909c-4c25-86ce-cceb11351840 · outbound

This paper cites Temporal meta-path guided explainable recommendation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Temporal meta-path guided explainable recommendation,

Reference 10

Resolution
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Source-reported events for the cited work

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Observation 0afda877-64c8-4000-a484-5e63796333d1 · outbound

This paper cites Path language modeling over knowledge graphsfor explainable recommendation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Path language modeling over knowledge graphsfor explainable recommendation,

Reference 11

Resolution
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Observation a637cfd5-3ab8-40bd-8700-1da1d879bddd · outbound

This paper cites Explainable session-based recommen- dation with meta-path guided instances and self-attention mechanism,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Explainable session-based recommen- dation with meta-path guided instances and self-attention mechanism,

Reference 12

Resolution
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Source-reported events for the cited work

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Observation 20101b8b-f247-4c26-b537-ec702b6034ee · outbound

This paper cites A comprehensive survey of neural architecture search: Challenges and solutions,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search A comprehensive survey of neural architecture search: Challenges and solutions,

Reference 13

Resolution
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Observation 6642c667-33ca-4c90-b4e0-ce36e5beb836 · outbound

This paper cites Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification,

Reference 14

Resolution
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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.

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Observation 735f977f-6dbe-45cb-966a-1767fdfc85fa · outbound

This paper cites Explicit factor models for explainable recommendation based on phrase-level sentiment analysis,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Explicit factor models for explainable recommendation based on phrase-level sentiment analysis,

Reference 15

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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.

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Observation b4fae509-564b-4a6e-9309-cf09079c813a · outbound

This paper cites Explainable recommendation via multi-task learning in opinionated text data,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Explainable recommendation via multi-task learning in opinionated text data,

Reference 16

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Observation 389da1b6-9c46-41a1-9fc3-b92d22f6dbce · outbound

This paper cites Try this instead: Personalized and interpretable substitute recommendation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Try this instead: Personalized and interpretable substitute recommendation,

Reference 17

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Observation f69e77b3-e0b5-42ca-b81a-94be5494316a · outbound

This paper cites Counterfactual explainable recommendation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Counterfactual explainable recommendation,

Reference 18

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Observation a858917e-06e5-4f10-bc20-55b237a8d0f4 · outbound

This paper cites From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems

Reference 19

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Observation 6e354caa-9118-4f39-a722-bce86a64cf74 · outbound

This paper cites Efficient neural interaction function search for collaborative filtering,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Efficient neural interaction function search for collaborative filtering,

Reference 20

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Observation 7b02b540-9437-4b83-92d1-cce07badbdf9 · outbound

This paper cites A Survey on Neural Architecture Search.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search A Survey on Neural Architecture Search

Reference 21

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Observation 6abb254e-fa7d-4829-b567-e741c723d519 · outbound

This paper cites Learning transferable architectures for scalable image recognition,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Learning transferable architectures for scalable image recognition,

Reference 22

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Source-reported events for the cited work

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Observation e56e0e99-6905-48ee-8ccd-ae06bc2cede3 · outbound

This paper cites Autoloss: Automated loss function search in recommendations,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Autoloss: Automated loss function search in recommendations,

Reference 23

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Observation bcd63594-2ce0-428c-a83b-c1b5088c3b24 · outbound

This paper cites Adafs: Adaptive feature selection in deep recommender system,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Adafs: Adaptive feature selection in deep recommender system,

Reference 24

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

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Observation 1d543c38-bd9e-4a8b-9f11-3419de726e3b · outbound

This paper cites Justifying recommendations using distantly-labeled reviews and fine-grained aspects,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Justifying recommendations using distantly-labeled reviews and fine-grained aspects,

Reference 25

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

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This paper cites The Llama 3 Herd of Models.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search The Llama 3 Herd of Models

Reference 26

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Observation 083d50ba-b906-4ffa-9dc0-404c7adee503 · outbound

This paper cites Neural col- laborative filtering.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Neural col- laborative filtering

Reference 27

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Source-reported events for the cited work

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Observation 0ed70c1a-6e1c-4989-8269-cd5159988f9a · outbound

This paper cites Vbpr: visual bayesian personalized ranking from implicit feedback.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Vbpr: visual bayesian personalized ranking from implicit feedback

Reference 28

Resolution
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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.

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Observation 084b1a93-7901-4740-b1a7-8019f1a671fc · outbound

This paper cites Based explainable recom- mendations: A transparency perspective,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Based explainable recom- mendations: A transparency perspective,

Reference 29

Resolution
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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.

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Observation 0a04a86e-c0b7-4a30-8480-483d51f89865 · outbound

This paper cites Learn basic skills and reuse: Modularized adaptive neural architecture search (manas),.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Learn basic skills and reuse: Modularized adaptive neural architecture search (manas),

Reference 30

Resolution
verified fuzzy
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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.

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Observation d9286292-3269-4aaf-9346-c05b7da8631e · outbound

This paper cites Explainable matrix factorization for col- laborative filtering,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Explainable matrix factorization for col- laborative filtering,

Reference 31

Resolution
verified fuzzy
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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.

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Observation e69a6586-ff8d-48db-81fa-596e10a38a3e · outbound

This paper cites Co- attentive multi-task learning for explainable recommendation.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Co- attentive multi-task learning for explainable recommendation

Reference 32

Resolution
verified fuzzy
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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.

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Observation b70975bd-c489-48bb-858c-d19b8e838114 · outbound

This paper cites Towards reliable rare category analysis on graphs via individual calibration,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Towards reliable rare category analysis on graphs via individual calibration,

Reference 33

Resolution
verified fuzzy
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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=2026-07-03T18:34:22.616679Z digest=sha256:99e9d553da6e904affb96e8c63bfe81f5712ef312c4648ea27fe0504ab5b2d36

Observation 83b8f82c-a94a-4e0a-915f-0a2d25a2b8d9 · outbound

This paper cites Towards trustworthy graph neural networks and their applica- tions in recommender systems,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Towards trustworthy graph neural networks and their applica- tions in recommender systems,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.682611Z

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=2026-07-03T18:34:22.616679Z digest=sha256:116de509bd2d12ea36ddfea785fc31efaf5917b56822c038b5d399141996c86e

Observation 344daacb-1c8d-45a1-a7c9-c1a0ef9c1d10 · outbound

This paper cites Trirank: Review-aware explainable recommendation by modeling aspects,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Trirank: Review-aware explainable recommendation by modeling aspects,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.706350Z

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=2026-07-03T18:34:22.616679Z digest=sha256:34d6525a40ed52d3ce4e723fd21b0ecfa7b7f70bcb0f806b42f9031f4ca101cc

Observation 6a3ff0ff-4075-499e-9ea4-3ec6e3e1d941 · outbound

This paper cites Joint deep modeling of users and items using reviews for recommendation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Joint deep modeling of users and items using reviews for recommendation,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.709190Z

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=2026-07-03T18:34:22.616679Z digest=sha256:84491501e5c73c22bcea530f97a50affd314624befea5614d74e85d17cd15e58

Observation cce6bdbe-882e-4ced-8daa-23774a4ca8e0 · outbound

This paper cites Transnets: Learning to transform for recommendation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Transnets: Learning to transform for recommendation,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.691409Z

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=2026-07-03T18:34:22.616679Z digest=sha256:352ffe293919344378a9b479710d372c6e80fc00c8d24c0fc775bd0bcd52d6a5

Observation fb125d04-a667-4fcf-a3c2-516339c95f35 · outbound

This paper cites Are vision llms road- ready? a comprehensive benchmark for safety-critical driving video understanding,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Are vision llms road- ready? a comprehensive benchmark for safety-critical driving video understanding,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.689164Z

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=2026-07-03T18:34:22.616679Z digest=sha256:7e97076dc05bf4665c9561fcb8e949d19e1192fc21f99ffd98f8a360cbb5a7b9

Observation ead9bd94-200e-4683-baf0-58e4ffe4cfac · outbound

This paper cites GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-07-03T18:38:48.755761Z

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=2026-07-03T18:34:22.616679Z digest=sha256:6726f55621c9b782db98bb3baeb434d6cb4e90cd8dfefc3421bf7138f7f83f58

Observation b1d6ac67-bc20-4823-8a41-8f4f09d148be · outbound

This paper cites Llm-generated explanations for recommender systems,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Llm-generated explanations for recommender systems,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.684647Z

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=2026-07-03T18:34:22.616679Z digest=sha256:a31af7ea8692ace6191c69dcf4fad9a2d31856bd6ee43f455ef6ce8a7e3d72e4

Observation 9c9a88b5-f3d9-432c-a71c-5986e220f859 · outbound

This paper cites Neural architecture search with reinforcement learning,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Neural architecture search with reinforcement learning,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.686685Z

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=2026-07-03T18:34:22.616679Z digest=sha256:ba965f7e75f3947ef53be501d4be5909ae6abc0502422f7e0f61f848c4b9b98a

Observation a369cedb-0208-429b-a335-ce2ecb19ec9c · outbound

This paper cites Efficient neural architecture search via parameters sharing.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Efficient neural architecture search via parameters sharing

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.711504Z

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=2026-07-03T18:34:22.616679Z digest=sha256:fefbd9245283339198c572760348e64980e612dde4d0fa3b102f88b0c39ff38f

Observation 23b75317-f5a7-456d-899f-4fa760b96745 · outbound

This paper cites Block-wisely supervised neural architecture search with knowledge distillation,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Block-wisely supervised neural architecture search with knowledge distillation,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.722835Z

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=2026-07-03T18:34:22.616679Z digest=sha256:d2f528a524bfc1ca9a6f17d79f46bca29d29a8fe69bab01de1d7b27ba380b7d1

Observation 512a8a76-f22d-46b8-9c36-4666d85efc9c · outbound

This paper cites DARTS: Differentiable Architecture Search.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search DARTS: Differentiable Architecture Search

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-07-03T18:38:48.771411Z

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=2026-07-03T18:34:22.616679Z digest=sha256:7a34f0dde6f5575f420a3fd532523358fae6a6758956c8229b4e3a8889afb457

Observation a19dc3d4-beb2-4768-b442-7b6098995b09 · outbound

This paper cites Autofield: Automating feature selection in deep recommender systems,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Autofield: Automating feature selection in deep recommender systems,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.698442Z

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=2026-07-03T18:34:22.616679Z digest=sha256:fadcdd56826f076102f4f0c8a3346fbb7f4bf18e01e97631c562c802d71d7b69

Observation cf5c7541-a1fd-4790-85af-5d705ad9452b · outbound

This paper cites Autocross: Automatic feature crossing for tabular data in real-world applications,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Autocross: Automatic feature crossing for tabular data in real-world applications,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.703529Z

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=2026-07-03T18:34:22.616679Z digest=sha256:dbbad3864b07bd6e2b9940ff3e9baf87e28ed60327037d134963d855d8828e2b

Observation 00b5ff60-afca-4020-8d40-28aa884ce63f · outbound

This paper cites Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.693530Z

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=2026-07-03T18:34:22.616679Z digest=sha256:c8554af81af276f60003b63ea4db52215240664a55f32e223082546ab4626398

Observation ed238043-2557-4cb2-ac1e-d8496bf88dc4 · outbound

This paper cites Nasrec: weight sharing neural architecture search for recommender systems,.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Nasrec: weight sharing neural architecture search for recommender systems,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-07-05T03:40:39.695887Z

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=2026-07-03T18:34:22.616679Z digest=sha256:6d664e4b05bd9e15231a483d0bbc3c7e93d1ae6eb4da2eb77203485671406333

Observation 817f0515-b374-490f-9deb-c424723d4a41 · outbound

This paper cites Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-07-03T18:38:48.765237Z

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=2026-07-03T18:34:22.616679Z digest=sha256:05877d4332a3d6839e2a826a7bcb72e3eef1be73164f512cf7706767e75cb4d7

Observation 64e2e5be-fee7-47b3-ad89-ed0866024f9d · outbound

This paper cites AutoML for Large Capacity Modeling of Meta's Ranking Systems.

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search AutoML for Large Capacity Modeling of Meta's Ranking Systems

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-07-03T18:38:48.753857Z

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=2026-07-03T18:34:22.616679Z digest=sha256:db3b0c233420a217d772a24dcd032c0c312e22f63475f117637220c7ca160403

Pith citing papers

No inbound Pith citation observations are available.