LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their promise, we identify a pervasive yet underexplored issue: $\textit{Length Bias}$. Because items are represented by textual descriptions of varying lengths, LLM-based recommenders can be systematically biased in two ways. On the input side, longer item descriptions occupy more tokens in the context and thus receive disproportionately large aggregate attention mass during user preference modeling. On the output side, decoding based on summed autoregressive log-likelihood score inherently disfavors long items. Worse s...
| Authors | Hongchen Li, Bohao Wang, Jingbang Chen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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