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This study introduces the Video E-commerce Retrieval Dataset (VERD) for improved product search using user videos. VERD addresses limitations in current image-based retrieval for complex scenarios and large datasets.

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Area of Science:

  • Computer Science
  • Information Retrieval
  • E-commerce Technology

Background:

  • The e-commerce market is rapidly expanding, increasing customer demand for effective product search.
  • Current object-centric retrieval methods using product images struggle with complex user environments and large data requirements.
  • There is a need for advanced retrieval techniques that can handle the nuances of real-world e-commerce interactions.

Purpose of the Study:

  • To introduce a novel dataset, the Video E-commerce Retrieval Dataset (VERD), for product search.
  • To facilitate research in product-centered video-based retrieval.
  • To address the limitations of existing image-based retrieval systems in e-commerce.

Main Methods:

  • Development of the Video E-commerce Retrieval Dataset (VERD) utilizing user-perspective videos.
  • Establishment of a benchmark for evaluating video-based product retrieval.
  • Conducting additional experiments to validate the dataset and retrieval approach.

Main Results:

  • The VERD dataset provides a valuable resource for studying product retrieval in complex scenarios.
  • The benchmark demonstrates the effectiveness of user-perspective videos for e-commerce search.
  • Experimental results highlight the need for dedicated research in video-based product retrieval.

Conclusions:

  • The Video E-commerce Retrieval Dataset (VERD) is a significant contribution to the field of e-commerce search.
  • VERD enables more robust and context-aware product retrieval compared to image-only methods.
  • The dataset is publicly available for academic research to foster further innovation.