AI RESEARCH
MemRerank: Preference Memory for Personalized Product Reranking
arXiv CS.AI
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ArXi:2603.29247v1 Announce Type: cross 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.