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AI
Machine Learning
Android
iOS

Food Shopping Mall Personalized Product Recommendation AI

TECHNOLOGIES

Python, TensorFlow, Keras, Flask, AWS

PLATFORMS

Android, iOS, Web

CUSTOMER

Confidential

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Project Background

The AI addresses the challenge of complex customer demands by collecting preference data through simple conversations. It learns both quantitative and qualitative criteria to provide precise recommendations, enhancing purchase rates and customer satisfaction.

Project Performance

  • Product page views increased approximately 10 times after implementing personalized recommendations.
  • Purchase process entry rate increased by approximately 7 times.
  • Purchase success rate increased by approximately 5 times.
  • Total sales increased by approximately 5 times.
  • The product range exposure expanded by about 3 to 4 times, reducing the burden of adding new categories.

Core Features

  • Automatic product updates and reflection in recommendations.
  • Personalized recommendations based on user preference data collected via chatbots.
  • User feedback integration to improve future recommendation accuracy.
  • Flexible recommendation criteria that can be quickly updated based on market conditions.

Progress Stage

  • Service requirements established in June 2023.
  • User interface and UX design completed in August 2023.
  • System design and AI integration finalized in September 2023.
  • Full feature development and commercialization preparation completed by October 2023.
  • Commercialization and user feedback integration finalized by January 2024.
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