ITM Web Conf.
Volume 44, 2022International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|Number of page(s)||5|
|Published online||05 May 2022|
Recipe Recommendation System Using TF-IDF
1 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai
2 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai
3 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai
4 Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University, Navi Mumbai
A Recipe Recommendation System is being proposed in this following paper. Food recommendation is a new area, with few systems that are focus on analysing and user preferences and constraints such as ingredients available at their side being deployed in real settings in the form of web application or mobile application . The proposed model is a mobile application which allows users to search recipes using ingredients available at them including vegetables. For this work we have find a dataset which is a collection of Indian cuisines recipes and apply the content-based recommendation using Term Frequency – Inverse Document Frequency (TF-IDF) and Cosine Similarity . This application gives the recommendation of Indian recipes based on ingredients available at them and allows users to filter out the recipes on course type, diet type, etc.
Key words: Machine Learning / Recommendation System / TF-IDF / Cosine Similarity / bag-of-words / NLP / Flask
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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