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

2025-03-28

AI Summary

This paper addresses the challenge of modeling group-level personalized preferences for AI-generated product images in e-commerce. We propose PerFusion—the first diffusion-model-oriented framework for group preference alignment. PerFusion innovatively integrates feature-cross reward modeling with adaptive preference optimization to enable fine-grained comparative preference estimation across multiple candidate images. Built upon a text-to-image diffusion model, the system delivers real-time, high-fidelity image generation, supporting a “sell-then-manufacture” paradigm. Online A/B testing demonstrates that AI-generated products outperform manually designed ones by over 13% in both click-through rate and conversion rate. The framework has been deployed at scale on Alibaba’s platform, significantly reducing inventory and prototyping costs. Key contributions include: (1) the first preference-aligned diffusion framework for e-commerce imagery; (2) a novel reward modeling approach enabling robust multi-candidate preference ranking; and (3) empirical validation of substantial business impact in live production environments.

Technology Category

Computer Vision: Diffusion Models for VisionHumans and AI: Learning Human Values and PreferencesMachine Learning: Learning Preferences or Rankings

Application Category

Economics, Online Markets and Human Computation: Cost models of using LLMs in production systemsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Reducing inefficiency in e-commerce product design workflows
• Capturing group-level user preferences for AI-generated items
• Enabling 'sell before production' with AI-generated fashion items

Innovation

• AI-generated items for e-commerce design
• Personalized group-level preference alignment
• Sell before production to reduce waste