UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models | Paper Citation Record

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 10

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 13

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Editing Models with Task Arithmetic

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Mixtral of Experts

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models GLU Variants Improve Transformer

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Scattered Mixture-of-Experts Implementation

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Venus-DeFakerOne: Unified Fake Image Detection & Localization

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 63

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction

Reference 64

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Reference 65

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Reference 66

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Reference 67

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Reference 68

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This paper cites PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference

Reference 69

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Reference 70

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Reference 71

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Reference 72

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Reference 73

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Reference 74

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Reference 75

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Reference 76

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UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Unresolved cited work

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-14T04:34:53.757477Z

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This paper cites In Findings of the Association for Computational Linguistics: ACL 2022.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models In Findings of the Association for Computational Linguistics: ACL 2022

Reference 80

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This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-14T04:34:53.804163Z

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Observation 742f7608-6845-4cb7-a264-9447c3da7de9 · outbound

This paper cites BPR: Bayesian Personalized Ranking from Implicit Feedback.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models BPR: Bayesian Personalized Ranking from Implicit Feedback

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-14T04:34:53.618590Z

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Observation 5dcb8c20-2b57-4830-8932-3e79b950e4cd · outbound

This paper cites Session-based Recommendations with Recurrent Neural Networks.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models Session-based Recommendations with Recurrent Neural Networks

Reference 2015

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This paper cites InProceedings of the 1st workshop on deep learning for recommender systems.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models InProceedings of the 1st workshop on deep learning for recommender systems

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-14T04:34:53.392593Z

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This paper cites InProceedings of the 28th ACM international conference on information and knowledge management.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models InProceedings of the 28th ACM international conference on information and knowledge management

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-14T04:34:53.649706Z

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Observation 86da643e-f146-44c2-9e1b-1e5eeba03ba5 · outbound

This paper cites InInternational Conference on Machine Learning.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models InInternational Conference on Machine Learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:34:55.221825Z

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Observation c2925a29-3fa2-4730-8d4c-66daeb613d5b · outbound

This paper cites In International conference on machine learning.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models In International conference on machine learning

Reference 2022

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This paper cites InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:34:55.336512Z

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Observation 8c735321-6422-4416-8a2c-9f00b72b9df6 · outbound

This paper cites doi:10.18653/v1/2025.findings-emnlp.169.

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models doi:10.18653/v1/2025.findings-emnlp.169

Reference 2025

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Observation d1f9a5b4-c4dc-4123-8ede-e2888c5a93e3 · outbound

This paper cites InProceedings of the 2026 International Conference on Mul- timedia Retrieval (ICMR ’26).

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models InProceedings of the 2026 International Conference on Mul- timedia Retrieval (ICMR ’26)

Reference 2026

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Coverage vector

Reference resolution

Outbound references

Pith citing papers