E-commerce has revolutionized retail, yet its traditional workflows remain inefficient, with significant resource costs tied to product design and inventory. This paper introduces a novel system deployed at Alibaba that uses AI-generated items (AIGI) to address these challenges with personalized text-to-image generation for e-commerce 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 users' group-level personalized preferences towards multiple generated images. To this end, we propose a Personalized Group-Level Preference Alignment Framework for Diffusion Models (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 model comparative behaviors among multiple images. Both offline and online experiments demonstrate the effectiveness of our proposed algorithm. The AI-generated items achieve over 13% relative improvements for both click-through rate and conversion rate, as well as 7.9% decrease in return rate, compared to their human-designed counterparts, validating the transformative potential of AIGI for e-commerce platforms.

翻译：电子商务已彻底改变了零售业，但其传统工作流程仍存在效率低下的问题，产品设计和库存管理消耗了大量资源。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），通过个性化的文本到图像生成技术来解决电子商务产品设计中的这些挑战。AIGI支持一种名为“先卖后造”的创新商业模式，商家可以根据文本描述设计时尚商品，并利用数字模特生成逼真的图像。只有当商品获得一定数量的订单后，商家才开始生产，这大大减少了对实体样品的依赖，从而加速了产品上市时间。针对这一前景广阔的应用，我们识别出其背后的关键科学挑战，即捕捉用户对多张生成图像的群体层面个性化偏好。为此，我们提出了一个用于扩散模型的个性化群体偏好对齐框架（PerFusion）。我们首先设计了一个基于特征交叉的个性化插件——PerFusion奖励模型，用于估计用户偏好。随后，我们开发了PerFusion，它包含一个个性化自适应网络，以建模不同用户的多样化偏好，同时推导出群体层面的偏好优化目标，以建模多张图像之间的比较行为。离线和在线实验均证明了我们提出算法的有效性。与人工设计的商品相比，AI生成商品的点击率和转化率均实现了超过13%的相对提升，同时退货率降低了7.9%，验证了AIGI对电子商务平台的变革性潜力。