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cs.IR2026
Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation
Shuo Chang, Yueqi Wang, Zihuan Diao +7
Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference. Although abundant, these signals are imperfect measurements: the same observed behav…
cs.IR2026
Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators
Zhengyang Su, Isay Katsman, Yueqi Wang +10
Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit from restricting the output space to a c…