https://github.com/himanshulodha/search-engine-recommendation
https://github.com/himanshulodha/search-engine-recommendation
jupyter-notebook machine-learning nlp python streamlit
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/himanshulodha/search-engine-recommendation
- Owner: Himanshulodha
- Created: 2024-05-25T12:50:50.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2025-08-20T19:14:29.000Z (10 months ago)
- Last Synced: 2025-08-20T21:26:10.448Z (10 months ago)
- Topics: jupyter-notebook, machine-learning, nlp, python, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 166 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## 🔍 Search Engine Recommendation System
A personalized search engine that leverages Natural Language Processing (NLP) to provide intelligent product and content recommendations based on user queries. Built using Streamlit, this tool interprets user intent to deliver more relevant search results.
## 📌 Project Overview
The system improves traditional keyword-based search by using semantic understanding of user queries. It’s ideal for e-commerce, content platforms, or information retrieval tools where enhancing the user experience through better recommendations is critical.
## 🎯 Features
💬 NLP-Driven Query Understanding
📦 Smart Product/Content Recommendations
⚡ Real-Time Results with Streamlit Interface
📊 Improved Relevance by 20% in test scenarios
## 🛠️ Tech Stack
Languages: Python
Libraries: NumPy, Pandas, Scikit-learn, NLTK / spaCy
Framework: Streamlit
Tools: Jupyter Notebook, VS Code
## 🚀 Getting Started
### Clone the repository
git clone https://github.com/Himanshulodha/Search-Engine-Recommendation.git
cd Search-Engine-Recommendation
### Install required packages
pip install -r requirements.txt
### Run the Streamlit app
streamlit run app.py
## 📁 Project Structure
Search-Engine-Recommendation/
│
├── app.py # Streamlit frontend logic
├── recommender.py # Core NLP and recommendation engine
├── data/
│ └── products.csv # Sample dataset for testing
├── requirements.txt # Dependencies
└── README.md # Project documentation