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The integration of artificial intelligence (AI) within the fashion industry is becoming increasingly prevalent, with the aim of delivering a shopping experience that is personalized, seamless, and engaging. As an example of this trend, GlamBot is an AI-driven fashion assistant, a sophisticated fashion assistant that employs a range of advanced AI methodologies. These methodologies include Natural Language Processing (NLP), image-based similarity searches, and voice recognition technologies, all of which together transform the interactions that users have with fashion platforms. The functionality of GlamBot significantly improves the user experience by providing tailored fashion recommendations. This is achieved through the analysis of text inputs, the execution of visual similarity searches, and the processing of voice commands. Consequently, fashion discovery is rendered more intuitive and accessible for users, thereby facilitating a more engaging interaction with the fashion domain. By analyzing and understanding user preferences, GlamBot builds personalized profiles that evolve over time to deliver increasingly accurate recommendations. GlamBot’s image-based search feature allows users to upload pictures of fashion items they like. Using advanced ResNet50 image recognition models, GlamBot analyzes these images and provides visually similar product recommendations, bridging the gap between users’ visual preferences and available fashion products.

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AI-Enhanced Fashion Assistant Featuring Image Recommendation - Glambot

  • 会议论文

  • 发表日期：

    2026年

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  • Praful Sambhare,
  • Nitin Choudhary,
  • Abhay Rahangdale,
  • Atharva Rane,
  • Sahil Raina

摘要

The integration of artificial intelligence (AI) within the fashion industry is becoming increasingly prevalent, with the aim of delivering a shopping experience that is personalized, seamless, and engaging. As an example of this trend, GlamBot is an AI-driven fashion assistant, a sophisticated fashion assistant that employs a range of advanced AI methodologies. These methodologies include Natural Language Processing (NLP), image-based similarity searches, and voice recognition technologies, all of which together transform the interactions that users have with fashion platforms. The functionality of GlamBot significantly improves the user experience by providing tailored fashion recommendations. This is achieved through the analysis of text inputs, the execution of visual similarity searches, and the processing of voice commands. Consequently, fashion discovery is rendered more intuitive and accessible for users, thereby facilitating a more engaging interaction with the fashion domain. By analyzing and understanding user preferences, GlamBot builds personalized profiles that evolve over time to deliver increasingly accurate recommendations. GlamBot’s image-based search feature allows users to upload pictures of fashion items they like. Using advanced ResNet50 image recognition models, GlamBot analyzes these images and provides visually similar product recommendations, bridging the gap between users’ visual preferences and available fashion products.