Author

Yudong Li

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Preprint Oct 2026

LoopGR: Parameter-Efficient Scaling for Generative Recommendation via Recurrent Depth

Generative recommendation formulates item recommendation as autoregressive generation rather than a discriminative scoring problem. Although empirical studies exhibit favorable scaling behavior in recommendation, unleashing the benefits of scaling remains challenging under the strict model size and inference cost const...

Hui-Zhe Zhang, Yu-Dong Li, Liang Chen et al. · 0 citations

Preprint Sep 2026

TGR: Advancing Industrial Recommendation from Generative-Paradigm Ranking toward Unified Generation and Reasoning

TGR (Tencent Generative Recommendation), an industrial framework that advances recommendation toward the generative paradigm along three coupled directions, is presented, which is deployed across Tencent production surfaces serving hundreds of millions of users.

T. Cheng, Hao-Nan Hu, Bei-Bei Kong et al. · 0 citations

Jul 2026

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation

BARGE is proposed, which employs Item Context-Aware Attention (ICA) to restore item-level structure during encoding, and Hierarchical Path Reranking (HPR) together with Dual-Path Decoding (DPD) to suppress semantic drift from two complementary angles during decoding.

Junchao Zeng, Junzhang Zhu, Junyang Chen et al. · arXiv.org · 1 citation