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cs.IR2026
One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets
Woosung Kang, Jiwon Jeong, Jonghyeok Shin +2
Existing sequential recommendation models rely on dataset-specific training, where the learned parameters are fitted to the item catalog and the observed interaction distribution o…
cs.IR2026
Every Preference Has Its Strength: Injecting Ordinal Semantics into LLM-Based Recommenders
Jiwon Jeong, Donghee Han, Sungrae Hong +2
Recent work has shown that large language models (LLMs) can enhance recommender systems by integrating collaborative filtering (CF) signals through hybrid prompting. However, most…