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This study leverages large language models (LLMs) to derive semantic priors for Thompson sampling in the context of cold-start comment recommendation, addressing the challenge of sparse interaction history for newly introduced user-generated comments. By implementing two distinct LLM-based priors鈥擥ender Prior and Content Prior鈥攖he authors demonstrate that these priors significantly enhance recommendation performance, particularly in early feedback scenarios. The results indicate that the LLM-derived priors not only improve engagement metrics but also highlight demographic differences in response patterns, suggesting tailored approaches can optimize user interaction.
LLM-derived priors can dramatically enhance recommendation systems in cold-start scenarios, revealing significant demographic response variations.
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-level differences in response patterns, we maintain and update posteriors separately for each gender-age segment. In a real-world online A/B/C test, we compare a uniform prior with two LLM-based designs: a Gender Prior for demographic-affinity cues and a Content Prior for title-specific identity cues. The results show that LLM-based priors are most beneficial in sparse-feedback regimes -- with the largest gains emerging once a small amount of interaction evidence has accumulated -- and that prior design leads to distinct funnel-level effects. We further analyze prior-reward alignment and demographic heterogeneity, finding that click-oriented alignment is strongest for the Gender Prior and that treatment effects vary substantially across demographic segments. These findings suggest that LLM-derived priors can serve as a practical warm-start mechanism for text-rich bandit recommendation, while also revealing deployment trade-offs.
