LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation
Multi-armed bandit algorithms, especially Thompson sampling, are widely used in online recommendation. Despite their ability to adapt from online feedback, these methods often suffer from cold-start limitations when newly introduced arms have little or no interaction history. In our setting, the candidate arms are user-generated textual comments, whose semantic content can reveal a title's appeal before sufficient interaction feedback is available. We therefore use large language models (LLMs) to extract semantic signals from comment text and convert them into informative Bayesian priors that warm-start Thompson sampling under sparse early-stage feedback. To account for aggregate segment-...
| Authors | Eugene Lee, Oseong Choi, Byungsoo Kang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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