# Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search

arXiv: https://arxiv.org/abs/2605.17994 · HTML: https://mlsanity.com/paper/2605.17994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 24 of 24)

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- [TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation](https://mlsanity.com/paper/2305.00447/) (2023)
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- [Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations](https://mlsanity.com/paper/2306.17256/) (2023)
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- [Large Language Model Simulator for Cold-Start Recommendation](https://mlsanity.com/paper/2402.09176/) (2024)
- [Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation](https://mlsanity.com/paper/2308.03470/) (2023)
- [TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios](https://mlsanity.com/paper/2508.11977/) (2025)
- [Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning](https://mlsanity.com/paper/2411.11225/) (2024)
- [AliBoost: Ecological Boosting Framework in Alibaba Platform](https://mlsanity.com/paper/2506.00954/) (2025)

## Abstract (arXiv)

New item growth is critical for maintaining a healthy ecosystem in large-scale e-commerce platforms. However, existing systems tend to prioritize presenting users with already popular items, a phenomenon often referred to as the "Matthew effect". In the context of search retrieval, current cold-start models suffer from the misalignment between training objectives and online business metrics, and they lack effective mechanisms to measure an item's growth potential. In this paper, we propose a Multi-Value-Aware retrieval framework tailored for e-commerce search, designed to better align with the cascaded online values across different stages of the search system while balancing immediate conversion and long-term item growth. Our framework GrowthGR consists of two key components: an Item Long-term Transaction Value Prediction (ItemLTV) module and a Multi-Value-Aware Generative Retrieval (MultiGR) module. First, in the ItemLTV module, we employ counterfactual inference to quantify the long-term value increment attributable to a single user interaction. Second, in the MultiGR module, building upon a semantic-ID-based generative retrieval architecture, we leverage structured samples with the search cascade signals and adopt a Multi-Value-Aware Policy Optimization (MoPO) training paradigm to align with multi-stage online values, while explicitly balancing short-term transactional value and long-term growth potential estimated by ItemLTV. We successfully deployed GrowthGR on Taobao's production platform, achieving a substantial 5.3% lift in new item GMV while delivering a non-trivial 0.3% gain in overall search GMV. Extensive online analysis and A/B testing demonstrate its positive impact on the overall ecosystem value.