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
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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