arXiv Machine Learning June 30, 2026 By Qingxiao Sun, Tongxuan Liu, Shen Zhang, Siyu Wu, Peijun Yang, Haotian Liang, Menxin Li, Xiaolong Ma, Zhiwei Liang, Ziyi Ren, Minchao Zhang, Yifan Wang, Xinyu Liu, Ke Zhang, Hailong Yang, Depei Qian

xGR: Efficient Generative Recommendation Serving at Scale

Read the original on arXiv Machine Learning →

arXiv:2512. 11529v3 Announce Type: replace Abstract: Recommendation system delivers substantial economic benefits by providing personalized predictions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

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arXiv:2608. 09685v1 Announce Type: new Abstract: Autoregressive semantic ID recommenders are constrained by expensive beam-search decoding, which limits the practical length of item identifiers.

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arXiv Machine Learning

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Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

arXiv:2602. 22647v2 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation.

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arXiv AI

Aug 3

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arXiv AI

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Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale

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arXiv Machine Learning

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Bumblebee: Interleaved Mixed-Layer Building Blocks for Large-Scale Recommendation Systems

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arXiv AI

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REPREC: Representation Driven Parameter-Efficient Recommendation System

arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.

By Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Kalanand Mishra

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