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This article helps to revolutionise food delivery system through introduction of user customisable meal ingredients while leveraging the capabilities of item-based collaborative algorithm for intelligent recommendation. It provides convenience around ingredient flexibility as well as enabling users to customise their meals based on their dietary preferences, diet restriction, nutritional requirements, etc. The recommendation engine utilises an item-based collaborative filtering algorithm in analysing specific customers and their food habits, thus suggesting a personalised meal option from historical data. The algorithm recognises both users and items similarities. The system is able to offer precise recommendations that match with individual tastes and preferences due to this fact. Due to its collaborative nature, users get new exciting food combinations that make the whole dining experience enjoyable. The aim of this research is to improve customer satisfaction, promote healthier eating choices, and support the development of food delivery market via integration cutting edge technology and concentration on personal taste.

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A Personalised Culinary Experience Through the Collaborative Filtering Algorithms and Ingredient Customisation

  • 会议论文

  • 发表日期：

    2025年

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  • Dhanraj Jadhav,
  • Supriya Mane,
  • Vinodkumar Bhutnal,
  • Tejas Kamble,
  • Aditya Barangule,
  • Rohit Bhangare,
  • Gowardhan Sharma

摘要

This article helps to revolutionise food delivery system through introduction of user customisable meal ingredients while leveraging the capabilities of item-based collaborative algorithm for intelligent recommendation. It provides convenience around ingredient flexibility as well as enabling users to customise their meals based on their dietary preferences, diet restriction, nutritional requirements, etc. The recommendation engine utilises an item-based collaborative filtering algorithm in analysing specific customers and their food habits, thus suggesting a personalised meal option from historical data. The algorithm recognises both users and items similarities. The system is able to offer precise recommendations that match with individual tastes and preferences due to this fact. Due to its collaborative nature, users get new exciting food combinations that make the whole dining experience enjoyable. The aim of this research is to improve customer satisfaction, promote healthier eating choices, and support the development of food delivery market via integration cutting edge technology and concentration on personal taste.