MemRerank: Preference Memory for Personalized Product Reranking
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.
69ce0a6facf91530b21dfa98
Zhiyuan Peng
69ce0a6facf91530b21dfa99
Xuyang Wu
69ce0a6facf91530b21dfa9a
Huaixiao Tou
69ce0a6facf91530b21dfa9b
Yi Fang
69ce0a6facf91530b21dfa9c
Yi Gong
MemRerank: Preference Memory for Personalized Product Reranking
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.
MemRerank is a preference memory framework that improves personalized product reranking by distilling user purchase history into concise, query-independent signals using reinforcement learning with downstream performance as supervision.
LLM-based shopping agents purchase history multi-turn interactions personalized product reranking preference memory framework memory extractor reinforcement learning downstream reranking 1-in-5 selection task end-to-end benchmark