|
|||||||||||||||
|
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
||||||||||||||
Issue: May 2024
Volume 9 | Issue 5
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 96
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Smart Food Recipe Ratings Prediction Using Revolutionizing Learning Techniques |
| Authors Name: | Shagufta Farhat , Syed Abdul Muqtadir Jaffari , Sumaiya Shaikh , Shaista Farhat , S. Zubair |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_210621
|
| Published Paper Id: | IJNRD2312126 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| 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. |
| Keywords: | Machine learning, smart food system |
| Cite Article: | "Smart Food Recipe Ratings Prediction Using Revolutionizing Learning Techniques", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 12, page no.b102-b106, December-2023, Available :http://www.ijnrd.org/papers/IJNRD2312126.pdf |
| Downloads: | 000118750 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID:IJNRD2312126 Registration ID: 210621 Published In: Volume 8 Issue 12, December-2023 DOI (Digital Object Identifier): Page No: b102-b106 Country: Sun City, Bandlaguda Jagir,Hyderabad, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2312126 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2312126 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn