User profiles, such as age and interest tags, form the backbone of modern recommender systems. However, in real-world scenarios, user profiles frequently encounter the problem of incomplete profile data, restricting the effectiveness of downstream recommendation tasks. Although large language models (LLMs) have shown r...
Riwei Lai, Yun-Sheng Xia, Li Chen et al.· 0 citations
Large Language Model-based Multi-Agent Systems (LLM-MAS) have shown exceptional promise for complex tasks, including retrieval-augmented generation and autonomous data analytics. However, their deployment in resource-constrained industrial environments faces critical challenges, such as unpredictable end-to-end latency...
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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