Recommender systems are essential for delivering personalized content across digital platforms by modeling user preferences and behaviors. Recently, large language models (LLMs) have been adopted for prompt-based recommendation due to their ability to generate personalized outputs without task-specific training. However, LLM-based methods face limitations such as limited context window size, inefficient pointwise and pairwise prompting, and difficulty handling listwise ranking due to token constraints. LLMs can also be sensitive to position bias, as they may overemphasize earlier items in the prompt regardless of their true relevance. To address and investigate these issues, we propose a hybrid framework that combines a traditional recommendation model with an LLM for reranking top-k items using structured prompts. We evaluate the effects of user history reordering and instructional prompts for mitigating position bias. Experiments on MovieLens-100K show that randomizing user history improves ranking quality, but LLM-based reranking does not outperform the base model. Explicit instructions to reduce position bias are also ineffective. Our evaluations reveal limitations in LLMs' ability to model ranking context and mitigate bias. Our code is publicly available at https://github.com/aminul7506/LLMForReRanking.

本研究针对传统推荐系统中存在的位置偏见问题，提出了一种结合传统推荐模型与大型语言模型(LLM)的混合框架，通过结构化提示对排名前k的项目进行重排序。尽管实验表明用户历史随机化改善了排名质量，但LLM重排序未能超越基础模型，且显式指令来减少位置偏见的效果也有限，揭示了LLM在建模排名上下文及减轻偏见方面的局限性。

基于提示的LLM在个性化推荐中的位置偏见感知重排序