manavisrani07 / Content-Based-Recommendation-System

Content-based recommendation system using sentence transformers.

Content-Based Recommendation System README

This README provides an overview of the Content-Based Recommendation System implemented using the Sentence Transformers library in Python. This system generates product recommendations based on the textual data and content features.

Table of Contents

• Overview
• Requirements
• Getting Started
• Usage
• Customization
• References

Overview

The Content-Based Recommendation System is designed to recommend products to users based on the textual features of the products, such as product names, descriptions, and product specifications. The system uses pre-trained models to encode textual data into embeddings and then calculates similarity scores to make recommendations.

Requirements

• Python 3.x
• Pandas
• Sentence Transformers
• NLTK (Natural Language Toolkit)

You can install the required Python packages using pip:

Getting Started

1. Clone the repository or download the code files to your local machine.

2. Create a virtual environment (optional but recommended):

3. Activate the virtual environment:

• On Windows:

• On macOS and Linux:

4. Run the recommendation system with your data:

# Create an instance of the recommendation system
recommender = ProductRecommendation()

# Fit the recommender with your data
recommender.fit(df)

# Make recommendations and enter exact product name
product_name = 'Product_Name'
recommendations = recommender.predict(product_name)

for rec in recommendations:
    print("->", rec)

This README provides an overview of the content-based recommendation system, its requirements, how to get started, and usage instructions.