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cs.LG2026
Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users
Wenhao Zheng, Wang Lu, Fangshuang Tang +4
Early-stage users in a new scenario intensify cold-start challenges, yet prior works often address only parts of the problem through model architecture. Launching a new user experi…
cs.LG2025
Anyprefer: An Agentic Framework for Preference Data Synthesis
Yiyang Zhou, Zhaoyang Wang, Tianle Wang +13
High-quality preference data is essential for aligning foundation models with human values through preference learning. However, manual annotation of such data is often time-consum…