MemRerank：基于偏好记忆的个性化商品重排序系统

MemRerank: Preference Memory for Personalized Product Reranking

March 31, 2026

作者: Zhiyuan Peng, Xuyang Wu, Huaixiao Tou, Yi Fang, Yi Gong

cs.AI

摘要

基于大语言模型的购物代理日益依赖长购买历史和多轮交互实现个性化，但直接将原始历史记录附加至提示往往因噪声干扰、长度过长及关联性失配而效果不佳。我们提出MemRerank偏好记忆框架，通过将用户购买历史提炼为简洁的查询无关信号来实现个性化商品重排序。为研究该问题，我们构建了以基于大语言模型的五选一任务为核心的端到端评估基准框架，同时衡量记忆质量与下游重排序效用。我们进一步采用强化学习训练记忆提取器，以下游重排序性能作为监督信号。在两种基于大语言模型的重排序器上的实验表明，MemRerank在无记忆、原始历史及现成记忆基线方法中持续领先，五选一准确率最高提升10.61个绝对百分点。这些结果证明显式偏好记忆是智能电商系统个性化实践中实用且有效的构建模块。

English

LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch. We propose MemRerank, a preference memory framework that distills user purchase history into concise, query-independent signals for personalized product reranking. To study this problem, we build an end-to-end benchmark and evaluation framework centered on an LLM-based 1-in-5 selection task, which measures both memory quality and downstream reranking utility. We further train the memory extractor with reinforcement learning (RL), using downstream reranking performance as supervision. Experiments with two LLM-based rerankers show that MemRerank consistently outperforms no-memory, raw-history, and off-the-shelf memory baselines, yielding up to +10.61 absolute points in 1-in-5 accuracy. These results suggest that explicit preference memory is a practical and effective building block for personalization in agentic e-commerce systems.