Semantic-ID-based generative recommendation represents each item as a short token sequence and trains an autoregressive model to predict the next item. This note asks whether Semantic ID tokens should be aligned with natural language or specialize into collaborative codes. We report two studies. First, representation p...
Shi-Teng Cao, Zhi-Heng Li· Proceedings of the 20th ACM...· 0 citations
Content-generation agents continuously receive impressions, clicks, conversions, and negative feedback from recommendation systems, providing real-world outcome signals for memory evolution. However, these signals are delayed and noisy, confounded by audience composition, placement, and recommendation policies, and may...
Shan-Wen Mao, Ming-Ming Li, Hao Zhang et al.· 0 citations
This work proposes Multi-Marginal Preference Optimization (MMPO), a fine-grained framework that intervenes at the data, gradient, and constraint levels rather than relying on coarse-grained global scalarization to address optimization conflicts among multiple objectives in real-world deployment scenarios.
Shang-Wen Mao, Hao Zhang, Guangtao Nie et al.· 1 citation· ⚡1
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