Long-sequence generative recommendation methods autoregressively model the user's interaction sequence to generate the next-item representation. Existing methods generally fall into two categories: efficient full-sequence modeling and target-aware context retrieval. Our experiments reveal that as the sequence length in...
Fei Li, Qing-Yun Gao, Jianzhe Zhao et al.· 0 citations
This paper presents GRIP (Generation and Reasoning from Incomplete Profiles), the first foundation model tailored for comprehensive profile inference under data sparsity for recommendation, and introduces a unified three-stage training framework.
Riwei Lai, Yun-Sheng Xia, Li Chen et al.· Proceedings of the 20th ACM...· 0 citations
G-STAR is a general graph-based scheduling framework that formalizes complex MAS pipelines as attributed Directed Acyclic Graphs (DAGs) and develops an industry-grade orchestration stack with asynchronous execution, resilient serving, and audit-friendly artifacts, offering a practical solution for optimizing web-scale...
Jia-Bao Song, Yun-Sheng Xia, Bei-Bei Kong et al.· Proceedings of the 32nd ACM...· 0 citations
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.
Tgr Team Lei Cheng, Hao-Nan Hu, Bei-Bei Kong et al.· 0 citations
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.
G-STAR is a general graph-based scheduling framework that formalizes complex MAS pipelines as attributed Directed Acyclic Graphs (DAGs) and develops an industry-grade orchestration stack with asynchronous execution, resilient serving, and audit-friendly artifacts, offering a practical solution for optimizing web-scale...
Jiabao Song, Yunsheng Xia, Beibei Kong et al.· Proceedings of the 32nd ACM...· 0 citations
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