Generative recommendation models based on autoregressive decoding have been adopted as alternatives to classical ranking-based systems. While their modeling flexibility has been widely studied, their resilience to data poisoning remains largely unexplored. In this work, we ask: how susceptible are generative recommende...
Zhuo-Xuan Zhang, Jinhao Duan, Xin-Yu Zhao et al.· 0 citations
Instead of rebuilding the entire recommendation stack from scratch, LIGE-GR generalizes the existing pointwise recommendation system into a listwise generation system, which allows mature recommender systems to benefit from listwise optimization while preserving compatibility with existing models, value functions, and...
Venkat Srinivas, Chen-Zhang He, Sam Woodmansee et al.· 0 citations
Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) long feedback loops, where model training can take days, making serial iteration prohibit...
Ming Li, Dai-Peng Li, Xu-Ying Ning et al.· 1 citation
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