Scholarlink

The challenge of time consumption during the furniture selection process is a significant pain point for many clients. Existing sources often fail to address this issue effectively, leading to prolonged decision-making and dissatisfaction. Our project aims to tackle this problem by presenting a comprehensive solution that combines cutting-edge technology with user-centric design. At the core of our solution is a requirement analysis for both new furniture and complementary items. This involves understanding the specific needs of the client and tailoring options accordingly. To facilitate a more engaging and efficient selection process, we incorporate 3D augmented reality (AR) technology. This allows clients to visualize furniture in their own spaces before making a decision, providing a more interactive and realistic experience. Our platform is designed to be multilingual, ensuring accessibility for a global audience and eliminating language barriers that might hinder user experience. This inclusivity is critical for reaching a diverse market and accommodating various linguistic preferences. A key component of our approach is the use of conditional generative adversarial networks (CGANs) alongside extensive image datasets. CGANs will generate high-quality, customizable furniture designs based on user preferences, text prompts, and input images, enabling clients to explore unique and tailored options. The image datasets will support this by offering a wide range of styles and variations, ensuring that clients have access to an extensive array of choices. By integrating these advanced technologies, we aim to streamline the furniture selection process, making it more efficient and enjoyable. The combination of 3D AR, multilingual support, and GANs with rich image datasets will provide a seamless, interactive experience that significantly reduces the time clients spend choosing the right furniture. Our solution represents a forward-thinking approach to enhancing client satisfaction and optimizing the decision-making process in the furniture industry.

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Recommendation Modeling for Complementary Furniture Products Based on Customer Preferences

  • 会议论文

  • 发表日期：

    2026年

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  • Sheetal Bukkawar,
  • Leena Rajan Katkar,
  • Aryan Sanjay Nagbanshi,
  • Aryan Subhash Dalvi,
  • Pushkar Manoj Mhatre,
  • Atharva Sushil Bhave

摘要

The challenge of time consumption during the furniture selection process is a significant pain point for many clients. Existing sources often fail to address this issue effectively, leading to prolonged decision-making and dissatisfaction. Our project aims to tackle this problem by presenting a comprehensive solution that combines cutting-edge technology with user-centric design. At the core of our solution is a requirement analysis for both new furniture and complementary items. This involves understanding the specific needs of the client and tailoring options accordingly. To facilitate a more engaging and efficient selection process, we incorporate 3D augmented reality (AR) technology. This allows clients to visualize furniture in their own spaces before making a decision, providing a more interactive and realistic experience. Our platform is designed to be multilingual, ensuring accessibility for a global audience and eliminating language barriers that might hinder user experience. This inclusivity is critical for reaching a diverse market and accommodating various linguistic preferences. A key component of our approach is the use of conditional generative adversarial networks (CGANs) alongside extensive image datasets. CGANs will generate high-quality, customizable furniture designs based on user preferences, text prompts, and input images, enabling clients to explore unique and tailored options. The image datasets will support this by offering a wide range of styles and variations, ensuring that clients have access to an extensive array of choices. By integrating these advanced technologies, we aim to streamline the furniture selection process, making it more efficient and enjoyable. The combination of 3D AR, multilingual support, and GANs with rich image datasets will provide a seamless, interactive experience that significantly reduces the time clients spend choosing the right furniture. Our solution represents a forward-thinking approach to enhancing client satisfaction and optimizing the decision-making process in the furniture industry.