Layout Generation for Various Scenarios in Mobile Shopping Apps

Title of the Paper

Layout Generation for Various Scenarios in Mobile Shopping Applications

Paper Information

• Subject Area: Human-Computer Interaction, Graphic Layout Generation, E-commerce
• Keywords: Layout generation, deep generative models, mobile application user interface, product listing pages, VQ-VAE, Transformer

Research Background and Problem Statement

• Identified Problems or Challenges:
  • The layout design of product listing pages (PLPs) in mobile shopping applications is generated under internal and external constraints, requiring a balance between consumer needs and the characteristics of shopping scenarios.
  • Creating large-scale, high-quality PLP layouts is a time-consuming and labor-intensive task. Traditional template-based methods struggle to meet the demands for diverse and customized designs.
  • Existing generative models rarely consider the impact of scenarios on layout design, making conditional layout generation challenging.
• Significance:
  • PLP layout design directly affects consumers' shopping experience, information retrieval efficiency, and purchasing decisions, playing a critical role in e-commerce platforms.
• Research Motivation and Related Work:
  • Traditional template-based methods cannot address the changing demands brought by the diversity of shopping scenarios. Deep generative models have shown promising results in layout generation in recent years but lack specialized research on shopping scenarios.
  • Existing studies, such as LayoutGAN and LayoutVAE, have made progress in graphic layout generation but fail to consider external constraints of shopping scenarios.

Solution

• Research Method or Solution:
  • A new design space is proposed to guide PLP layout creation, analyzing different shopping scenarios from three dimensions: consumer behavior and psychology, product information needs, and layout patterns.
  • A novel generative model, LayoutVQ-VAE, is proposed, combining VQ-VAE and Transformer networks to achieve discrete layout representation learning under internal and external constraints.
  • A dataset containing 2,575 annotated product card layouts (PDCard) was constructed to facilitate the analysis of explicit layout features.
• Innovations:
  • For the first time, shopping scenarios are introduced as external constraints into PLP layout generation, with layouts represented using discrete latent variables.
  • VQ-VAE is employed to prevent the "posterior collapse" problem in latent variable models, while Transformers are used to model the relationship between scenarios and layouts.
• Implementation Steps and Key Techniques:
  1. Dataset analysis and construction: Create the PDCard dataset with clearly annotated scenarios and elements.
  2. Modeling: Use VQ-VAE for discrete layout representation and combine it with Transformers to capture the relationship between layouts and constraints.
  3. Experimentation and validation: Compare the performance of the proposed method with existing approaches on public datasets and the PDCard dataset.

Research Outcomes

• Specific Outcomes:
  1. A design space was proposed that comprehensively considers shopping contexts, consumer behavior, and product information to guide PLP layout design.
  2. The LayoutVQ-VAE model demonstrated superior performance in layout generation and reconstruction tasks:
     • In generation experiments, the model outperformed existing methods in terms of rationality, aesthetics, and scenario relevance scores.
     • Achieved better FID and MaxIoU metrics on public datasets such as Publaynet and Rico compared to state-of-the-art techniques.
  3. On the PDCard dataset, the model was able to generate diverse, high-quality layouts tailored to different scenarios.
• Advantages:
  • The generated layouts significantly enhance scenario relevance through constraints and can be directly applied to various mobile shopping contexts.
  • The model significantly reduces layout generation time (an average of 0.138 seconds per layout).
• Experimental or Evaluation Results:
  • The model effectively generates diverse, high-quality layouts matching given scenarios, demonstrating good generation speed and low algorithmic complexity.
  • Two user studies validated the method's applicability and efficiency, with participants showing a preference for scenario-based generated layouts.
• Limitations and Future Directions:
  • Limitations: The current study focuses primarily on three scenarios and does not deeply explore the impact of product types and broader user characteristics on layout design.
  • Future Directions: Investigate large-scale, real-time PLP layout generation guided by personalized consumer needs to achieve intelligent user interface design.