Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items

• URL: http://arxiv.org/abs/2503.22182v1
• Date: Fri, 28 Mar 2025 07:00:33 GMT
• Title: Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items
• Authors: Jianghao Lin, Peng Du, Jiaqi Liu, Weite Li, Yong Yu, Weinan Zhang, Yang Cao,
• Abstract summary: 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.<n>We propose a Personalized Group-Level Preference Alignment Framework for Diffusion Models (i.e., PerFusion) to capture the users' group-level personalized preferences towards multiple generated candidate images.<n>The AI-generated items have achieved over 13% relative improvements for both click-through rate and conversion rate compared to their human-designed counterparts.
• Score: 41.69406276304483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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