6 citations · 13 across the 8 of their papers we have counts for
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cs.IR2026
Accelerating Generative Recommendation via Simple Categorical User Sequence Compression
Qijiong Liu, Lu Fan, Zhongzhou Liu +7
Although generative recommenders demonstrate improved performance with longer sequences, their real-time deployment is hindered by substantial computational costs. To address this…
cs.CL2026
Judging with Personality and Confidence: A Study on Personality-Conditioned LLM Relevance Assessment
Nuo Chen, Hanpei Fang, Piaohong Wang +3
Recent studies have shown that prompting can enable large language models (LLMs) to simulate specific personality traits and produce behaviors that align with those traits. However…