Restaurant Recommendation System

This repository contains a set of code files for building a Restaurant Recommendation System using multiple techniques, including Content-Based Filtering, Collaborative Filtering, and Hybrid Models. The system provides personalized restaurant recommendations based on user preferences and historical data.

Dataset Link:

https://www.kaggle.com/datasets/vora1011/zomato-bangalore-restaurants-2022/

Repository Structure

1. Code Files:

   • 1_RecomSystem_knowledge_based.py: Implements knowledge-based recommendation logic.
   • 2_RecomSystem_Content_User_Entry.py: Allows user input for content-based recommendations.
   • 2_RecomSystem_Restaurant_Content.py: Implements content-based filtering using restaurant features.
   • 3_Matrix_Multiplication.ipynb: Jupyter notebook for matrix multiplication operations.
   • 3_RecomSystem_Matrix_Multiplication.py: Python script for matrix multiplication.
   • 4_Hybrid_Recommendation_model.ipynb: Jupyter notebook for building a hybrid recommendation model.
   • 4_RecomSystem_Hybrid.py: Python script for hybrid recommendation logic.
   • 5_Collaborative_Filtering.ipynb: Jupyter notebook for collaborative filtering model.
   • 5_RecomSystem_Collaborative.py: Python script for collaborative filtering model.

2. Data Files:

   • BangaloreZomatoData.csv: Raw data for restaurants in Bangalore.
   • BangaloreZomatoData_with_rest_id.csv: Processed data with restaurant IDs.
   • UserOrdersData.csv: Data for user orders and ratings.
   • USER AND RESTRAUNT.xlsx: Additional data for user and restaurant interactions.

3. README.md: This file.

Features

• Knowledge-Based Filtering: A knowledge-based recommender system (KBRS) is a decision support system that uses explicit knowledge about items, users, and recommendations to help users find relevant items.
• Content-Based Filtering: Recommends restaurants based on their features like cuisines and what they are known for.
• Collaborative Filtering: Uses user-item interactions to recommend restaurants based on user ratings.
• Hybrid Model: Combines both content-based and collaborative filtering techniques for better recommendations.
• Matrix Multiplication Based: The Matrix Multiplication-Based Restaurant Recommendation System helps users find suitable restaurants based on their preferences.

Technologies Used

• Python: Programming language used for the implementation.
• Pandas: For data manipulation and processing.
• Scikit-learn: For machine learning models like cosine similarity and SVD.
• Surprise: For collaborative filtering using the SVD algorithm.
• Tkinter: For building the graphical user interface (GUI).
• Jupyter Notebooks: For matrix multiplication and hybrid recommendation model development.

Installation

1. Clone the repository:

   git clone https://github.com/prateekmaj21/Restaurant-Recommendation-System.git

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the Tkinter app to interact with the recommendation system.

Future Improvements:

• Integrating additional recommendation algorithms.
• Adding more user interaction features.
• Expanding the dataset to include more restaurants and user interactions.