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

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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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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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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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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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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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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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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

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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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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

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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

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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

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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

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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

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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

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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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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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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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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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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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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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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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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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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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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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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

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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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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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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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,

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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

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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

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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

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

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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,

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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,

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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,

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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,

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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

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

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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

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

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Pith citing papers