E-commerce has revolutionized retail, yet its traditional workflows remain inefficient, with significant time and resource costs tied to product design and manufacturing inventory. This paper introduces a novel system deployed at Alibaba that leverages AI-generated items (AIGI) to address these challenges with personalized text-to-image generation for e-commercial product design. AIGI enables an innovative business mode called "sell it before you make it", where merchants can design fashion items and generate photorealistic images with digital models based on textual descriptions. Only when the items have received a certain number of orders, do the merchants start to produce them, which largely reduces reliance on physical prototypes and thus accelerates time to market. For such a promising application, we identify the underlying key scientific challenge, i.e., capturing the users' group-level personalized preferences towards multiple generated candidate images. To this end, we propose a Personalized Group-Level Preference Alignment Framework for Diffusion Models (i.e., PerFusion). We first design PerFusion Reward Model for user preference estimation with a feature-crossing-based personalized plug-in. Then we develop PerFusion with a personalized adaptive network to model diverse preferences across users, and meanwhile derive the group-level preference optimization objective to capture the comparative behaviors among multiple candidates. Both offline and online experiments demonstrate the effectiveness of our proposed algorithm. The AI-generated items have achieved over 13% relative improvements for both click-through rate and conversion rate compared to their human-designed counterparts, validating the revolutionary potential of AI-generated items for e-commercial platforms.

翻译：电子商务已彻底改变零售业，但其传统工作流程仍存在效率低下的问题，产品设计和制造库存消耗了大量时间和资源成本。本文介绍了一种在阿里巴巴部署的新型系统，该系统利用AI生成商品（AIGI），通过面向电商产品设计的个性化文生图技术应对这些挑战。AIGI支持一种名为“先销后产”的创新商业模式：商家可根据文本描述设计时尚商品，并生成搭载数字模特的逼真图像。仅当商品达到一定订单量后，商家才开始生产，这大幅降低了对实体样品的依赖，从而加速上市进程。针对这一前景广阔的应用，我们揭示了其核心科学挑战——捕捉用户群体对多个生成候选图像的个性化偏好。为此，我们提出了面向扩散模型的个性化群体偏好对齐框架（PerFusion）。我们首先设计了基于特征交叉个性化插件的PerFusion奖励模型用于用户偏好估计；继而开发了配备个性化自适应网络的PerFusion框架以建模用户间的差异化偏好，同时推导出群体偏好优化目标以捕捉多候选方案间的对比行为。离线和在线实验均证明了所提算法的有效性。与人工设计商品相比，AI生成商品的点击率和转化率均实现超过13%的相对提升，验证了AI生成商品对电商平台的革命性潜力。