Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items (2503.22182v1)

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.

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

The academic paper titled "Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items" by Jianghao Lin and colleagues introduces a novel approach for leveraging AI in the e-commerce industry. The work focuses on a system deployed at Alibaba, intended to optimize the workflow of product design and reduce inefficiencies in traditional retail processes. Key elements of this approach involve utilizing AI-generated items (AIGI) through text-to-image generation models, fostering a business model encapsulated in the concept of "sell it before you make it."

AI-Generated Items and E-Commerce

The core innovation presented is the deployment of AI models that can generate photorealistic images of products based solely on textual descriptions provided by merchants. This bypasses traditional processes that involve the time-consuming tasks of designing, manufacturing, and photographing products before they are introduced to the market. The AI-generated images allow for consumer interest to be gauged prior to any physical production, minimizing risk, reducing waste, and significantly accelerating time to market. Notably, the introduction of this system has demonstrated a notable increase in performance metrics such as click-through rates (CTR) and conversion rates, capturing over 13% improvement compared to human-designed counterparts.

Challenges and Methodology

The authors identify several scientific challenges inherent in implementing such a system, primarily focusing on user preference prediction and image generation. They introduce the Personalized Group-Level Preference Alignment Framework for Diffusion Models, termed as PerFusion. This framework centers around two main objectives: predicting user preferences through a PerFusion Reward Model (PerFusionRM) and generating personalized product images via a model based on the diffusion process.

1. PerFusion Reward Model (PerFusionRM): This is designed to estimate user preferences with personalization, utilizing a feature-crossing-based approach. It accounts not only for text-image consistency but also for individual user preferences, integrating this knowledge directly into a modified version of the CLIP model.
2. Diffusion Model Framework: PerFusion applies diffusion models to generate images that align with user's group-level personalized preferences. A personalized adaptive network is integrated to capture diverse preferences across users, with a group-level preference optimization objective that models user comparative behavior patterns.

Experimental Evaluation

In both offline and online settings, the methodologies proposed have been rigorously tested against various baselines. PerFusionRM outperformed other models in estimating user preferences and ranking tasks, with substantial advancement observed especially in industrial contexts as opposed to publicly sourced datasets. In terms of image generation, PerFusion achieved superior scores across several metrics, including aesthetic evaluation, alignment with human preferences, and personalized engagement, demonstrating the framework's robustness and versatility.

Implications and Future Directions

This research holds significant implications for the future of e-commerce, particularly how products are marketed and sold. By employing AI-generated items that capture and reflect customer preferences accurately, companies can drastically reduce overhead costs associated with product development while improving responsiveness to market dynamics and consumer trends. Furthermore, the "sell it before you make it" paradigm highlights potential shifts in retail business strategies, potentially reshaping supply chain management to emphasize digital over physical assets initially.

Future studies may focus on refining AI models to enhance user personalization and address edge cases in diverse markets. The continued integration of AI into e-commerce platforms paves the way for even more innovative applications, potentially influencing other industries where design and consumer perception are critical. This research underscores the transformative potential of AI technologies in aligning more closely with consumer expectations and needs, offering a template for integrating cutting-edge solutions into traditional business models.