RecGroupMemory: Feedback-Aware Cohort Memory for E-Commerce Recommendation

Cohort memory can support LLM-based recommendation planning through group-level user profiles and advice. As outcome feedback becomes available, this guidance may need to be updated while preserving the underlying observations. We present RecGroupMemory, a workflow that provides this memory to an orchestration LLM and separates memory construction from feedback-based revision. It uses feedback from the previous day to revise decision guidance, while validation checks keep selected source values unchanged. Online comparisons were conducted among Taobao users with low purchasing power selected by the business team. Relative to a no-memory baseline, memory without feedback and memory with feedback showed increases of 1.58% and 1.78%, respectively, in the number of guided transaction users.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-11-02
DOI
https://doi.org/10.48550/arxiv.2611.02453
Primary Topic
Recommender Systems and Techniques
Type
preprint
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preprint

RecGroupMemory: Feedback-Aware Cohort Memory for E-Commerce Recommendation

Dimin Wang, Weipeng Huang, Wendong Zhang, Zhixin Ma et al.
arXiv (Cornell University)
Recommender Systems and Techniques
preprint

RecGroupMemory: Feedback-Aware Cohort Memory for E-Commerce Recommendation

Dimin Wang, Weipeng Huang, Wendong Zhang, Zhixin Ma, Bin Zhang
preprint en

Abstract

Cohort memory can support LLM-based recommendation planning through group-level user profiles and advice. As outcome feedback becomes available, this guidance may need to be updated while preserving the underlying observations. We present RecGroupMemory, a workflow that provides this memory to an orchestration LLM and separates memory construction from feedback-based revision. It uses feedback from the previous day to revise decision guidance, while validation checks keep selected source values unchanged. Online comparisons were conducted among Taobao users with low purchasing power selected by the business team. Relative to a no-memory baseline, memory without feedback and memory with feedback showed increases of 1.58% and 1.78%, respectively, in the number of guided transaction users.

arXiv (Cornell University)
Recommender Systems and Techniques
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