This document presents a content-based food recommendation system designed for Indian culinary tastes, detailing its motivation, objectives, literature survey, and methodology. It outlines the different types of recommendation systems, including content-based, collaborative filtering, and hybrid systems, and discusses their implementation using a dataset from Kaggle. The conclusion highlights potential future improvements such as enhanced algorithms, user feedback integration, and extensions to other cuisines.

1. Indian Recipe Recommendation System
A Content-Based Food Recommendation System for
Diverse Indian Culinary Tastes
Prepared By
Neha Tyagi
M. Tech (CE) 4th
Sem.
Roll no-MRT22PGMCS001

2. Agenda
• Introduction
• Motivation & Objectives
• Background
• Literature Survey
• Methodology Used
• Evaluation Principles
• Implementation
• Conclusion and Future Directions

3. What are Recommendation Systems?
• It is an information filtering tool designed to predict a user's
preferences or ratings for specific items.
• These are filtering tools that provide recommendations that might
interest the users.
• Recommendations provided are based on their likes or the likes of
similar users.
Some Popular Websites that use Recommendation System
Fig 1 . Recommendation System

4. Popular Websites
Websites What is recommended?
Facebook Friend Suggestions
YouTube Videos
Amazon Consumer Products
Netflix Movies and Web series

5. Motivation & Objectives
Motivation
 Technological Advancements and AI
Utilization
 Economic Motivation
 Enhancing Culinary Skills and
Creativity
 Social and Cultural Connectivity
Objectives
 Business and Marketing
 Enhancing User Experience
 Cultural and Culinary Exploration
 Academic and Research Purposes

6. Types of
Recommendation
Systems
1. Content-based Recommendation Systems
2. Collaborative Filtering (CF) based
Recommendation System
3. Hybrid Recommendation System
Basic Models of Recommendation System
• User-item interactions
• Attribute information about the users and
items

7. Content-based
Recommendation
Systems
Content filtering uses the attributes or features
of an item to recommend other items similar to
the user’s preferences.
Click icon to add picture
Fig 2. Content Based Recommendation Systems

8. Collaborative
Filtering based
Recommendation
Systems
Click icon to add picture
Collaborative filtering algorithms recommend items
(this is the filtering part) based on preference
information from many users
(this is the collaborative part).
Fig 3. Collaborative Filtering Based
Recommendation Systems

9. Hybrid
Recommendation
Systems
Hybrid recommendation Systems combine the
advantages of both types to create a more
comprehensive recommendation system.
Click icon to add picture
Fig 4. Hybrid Recommendation
Systems

10. Literature Survey
Researchers Method Used Contributions Limitations
G. Sekhar Babu* et al.
(2024)
Graph Clustering,
Machine Learning
Novel hybrid food
recommender-system
addresses key issues for
user satisfaction.
cold start users, food
items, and community
aspects.
G Prabhakar* et al.
(2024)
Ensemble model with
DTC, SVM, and ANN for
diabetes prediction
Bagging ensemble
classifier for type-2
diabetes prediction
Data privacy concerns and
unclear real-time
capabilities in healthcare
monitoring
Kudang Boro* et al.
(2023)
Genetic Algorithm with
elitism for food
recommendation model
Introduces a novel food
recommender system
using genetic algorithm,
Elitism in Genetic
Algorithm leads to slower
convergence rate.

11. Methodology Used
 Dataset sourced from
https://www.kaggle.com/datasets/sooryaprakash12/
cleaned-indian-recipes-dataset
 The Term Frequency-Inverse Document Frequency (TF-
IDF) is used to convert text into numerical
representations.
 By computing cosine similarity between pairs of recipes
 For a given query, the recommendation engine utilized
the computed cosine similarity matrix
 Recipes with the highest similarity scores are
recommended
 Data Collection and Pre-
processing
 Text Vectorization
 Similarity Computation
 Generate Recommendations
 Implement and Deploy
 Evaluation

12. Dataset
Fig 5.Dataframe Info
Fig 6. CSV View

13. Evaluation Principles
 Evaluating a recommendation system involves two main aspects :
 Relevance of Recommendations
 Ranking Quality
 Metrics for Explicit Feedback Recommendation Systems
 Mean Absolute Error, Root Mean Square Error, etc.
 Metrics for Implicit Feedback Recommendation Systems
 Precision@K, Recall@K, Mean Average Precision (MAP), Mean
Average Recall (MAR)
Some Popular Websites that use Recommendation System

14. Software Requirements
1) PyCharm: An Integrated Development Environment (IDE) for Python
programming.
2) Streamlit: A powerful library for building interactive web applications with
Python, ideal for creating user-friendly interfaces and visualizing data-driven
insights effortlessly.
3) Pandas: Enables data analysis and manipulation, offering efficient data
structures and operations for large files like CSV or TSV.
4) Scikit-learn: Provides various machine learning algorithms for data analysis
and modeling tasks.
5) Numpy: Facilitates handling large, multi-dimensional arrays and matrices and
supports high-level mathematical functions.

15. UI Screens
Some Popular Websites that use Recommendation System
Fig 8. Search Screen

16. UI Screens
Some Popular Websites that use Recommendation System
Fig 9. Results Screen

17. Conclusion and Future Directions
• To conclude, our system has shown promising results, but there are
several avenues for future research and development:-
 Enhanced Recommendation Algorithms
 Integration of User Feedback
 Expansion to Other Cuisines
 Integration with Cooking Platforms
 Personalized Nutritional Recommendations

18. Thank you
Neha Tyagi
M. Tech (CE)
Roll no-MRT22PGMCS001