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.

翻译：新品增长对于维护大规模电商平台的健康生态至关重要。然而，现有系统往往倾向于优先向用户展示已有流行商品，这种现象常被称为“马太效应”。在搜索检索场景中，当前冷启动模型存在训练目标与在线业务指标不一致的问题，且缺乏有效衡量商品增长潜力的机制。本文提出了一种面向电商搜索的多价值感知检索框架，旨在更好地对齐搜索系统不同阶段的级联在线价值，同时平衡即时转化与长期商品增长。我们的框架GrowthGR由两个关键组件构成：商品长期交易价值预测模块（ItemLTV）和多价值感知生成式检索模块（MultiGR）。首先，在ItemLTV模块中，我们采用反事实推理来量化单次用户交互所带来的长期价值增量。其次，在MultiGR模块中，基于语义ID的生成式检索架构，我们利用包含搜索级联信号的结构化样本，并采用多价值感知策略优化（MoPO）训练范式，以对齐多阶段在线价值，同时显式平衡由ItemLTV估计的短期交易价值与长期增长潜力。我们成功将GrowthGR部署于淘宝生产平台，在实现新品GMV显著提升5.3%的同时，带动整体搜索GMV增长0.3%。广泛的在线分析与A/B测试验证了其对整体生态价值的积极影响。