Author

Qiu-Ling Xu

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#artificial intelligence Preprint Oct 2026

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to recommend, where each SID is a short sequence of discrete hierarchical content codes. A single instruction-tuned model can then unify retrieval, content understanding, and...

Sejoon Oh, Fernando Amat Gil, Evan Smoth-ers et al. · 0 citations

Preprint Aug 2026

Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback

This work proposes Exponential reward-weighted fine-tuning (Exp-RSFT), where each logged interaction is weighted by $\exp(r/\lambda)$, and predicts performance follows an inverted-U trend as a function of $\lambda$, while PPO and DPO often over-optimize unreliable reward models and degrade recommendation quality.

Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiu-Ling Xu et al. · 0 citations