Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search
Yifan Wang, Yixuan Wang, YiDan Liang, Qiang Liu, Fei Xiao

TL;DR
This paper introduces GrowthGR, a multi-value-aware retrieval framework for e-commerce search that balances immediate conversions and long-term item growth, demonstrated by significant improvements on Taobao.
Contribution
The paper presents a novel framework combining long-term value prediction and multi-value-aware generative retrieval to enhance new item growth in e-commerce search.
Findings
5.3% increase in new item GMV on Taobao
0.3% overall search GMV gain
Positive impact demonstrated through extensive online testing
Abstract
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…
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