Paper Title
Smart Food Recipe Ratings Prediction Using Revolutionizing Learning Techniques
Article Identifiers
Authors
Shagufta Farhat , Syed Abdul Muqtadir Jaffari , Sumaiya Shaikh , Shaista Farhat , S. Zubair
Keywords
Machine learning, smart food system
Abstract
In the era of Information technology and the growing influence of social media, the culinary landscape is evolving rapidly. Smart food recipe platforms have become essential for individuals seeking diverse and personalized cooking experiences. This study presents a novel approach to enhance user engagement by predicting recipe ratings based on user preferences and behavior. The proposed smart food recipe rating prediction system leverages machine learning algorithms to analyze vast datasets of user interaction with recipes. By considering factors such as ingredient choices, preparation steps, and historical user ratings, the system employs a predictive model to estimate the potential rating a recipe might receive from a user. The predictive model is continually refined through user feedback, ensuring adaptive and accurate recommendations over time. This system integrates natural language processing techniques to understand user reviews, extracting sentiment and identifying key features that contribute to positive or negative evaluations. Additionally, collaborative filtering mechanisms are employed to identify patterns in user behavior and recommend recipes based on the preferences of users with similar tastes. This research contributes to the field of smart food technology by offering an intelligent system that not only recommends recipes but also predicts user specific ratings, providing a more personalized and enjoyable cooking experience. The proposed system has the potential to revolutionize the way individuals discover and engage with culinary content in the digital age.
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How To Cite (APA)
Shagufta Farhat, Syed Abdul Muqtadir Jaffari, Sumaiya Shaikh, Shaista Farhat, & S. Zubair (December-2023). Smart Food Recipe Ratings Prediction Using Revolutionizing Learning Techniques. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(12), b102-b106. https://ijnrd.org/papers/IJNRD2312126.pdf
Issue
Volume 8 Issue 12, December-2023
Pages : b102-b106
Other Publication Details
Paper Reg. ID: IJNRD_210621
Published Paper Id: IJNRD2312126
Downloads: 000121985
Research Area: Computer Science & TechnologyÂ
Country: Sun City, Bandlaguda Jagir,Hyderabad, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2312126.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312126
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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