arXiv:2603.29247v3 Announce Type: replace-cross Abstract: 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 \textbf{1-in-5} selection task, which me
The proliferation of LLM-based applications in e-commerce necessitates more sophisticated methods for personalization that overcome basic prompt engineering limitations.
Improved personalization models based on distilled user preferences will significantly enhance the efficiency and effectiveness of AI shopping agents, driving higher conversion rates and customer satisfaction.
Product recommendation and reranking systems can now move beyond raw history appending, allowing for more nuanced and performant personalization through preference memory frameworks.
- · E-commerce platforms
- · AI shopping agent developers
- · Consumers
- · Recommendation engine providers
- · Simple rule-based recommendation systems
- · Systems heavily reliant on raw data inputs
Personalized shopping experiences will become significantly more accurate and less prone to irrelevant suggestions.
This improved personalization could lead to increased market share for platforms that effectively implement such advanced AI agents.
The success of distilled preference memory could inspire similar approaches in other AI applications facing information overload and context window constraints.
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Read at arXiv cs.AI