This repository will explain the basic implementation of different types of Recommendation systems using python.
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Updated
Sep 18, 2018 - Jupyter Notebook
This repository will explain the basic implementation of different types of Recommendation systems using python.
Hotel Recommendation system based on Content, Collaborative, Social Network Based Systems
🌟 Production-ready AI news aggregation API with 11 intelligent features ✨ | Multi-source aggregation (70+ sources) | Gemini AI enhancement | Personalized recommendations | Built with Node.js + TypeScript + MongoDB 🚀
AI-powered article recommender using sentence embeddings and FAISS for semantic search. Includes a FastAPI backend and Streamlit frontend.
🤖📚 Machine learning model which predicts the likability of unread storybooks based on a child's previously read storybooks.
Recommendation System & it's types
Help sales teams send the right content at the right moment. This n8n workflow automation analyzes deal context from Zoho CRM, uses GPT-4o-mini to recommend personalized content and delivers suggestions via Gmail. A smart n8n workflow template for improving deal relevance, follow-ups and close rates.
A trust based social network for user engagement and protection.
A full-stack recommender platform featuring a FastAPI backend and a React/TypeScript frontend. It utilizes TF-IDF and Cosine Similarity to process movie metadata into real-time suggestions, supported by a SQLite database and a modern Vite-powered UI.
Content, Collaborative, and Hybrid Movie recommendation system
Interactive Power BI dashboard analyzing Netflix titles, viewing patterns, and strategic recommendations.
🚀 Generate tailored deal content recommendations using Zoho CRM, GPT-4o-mini, and Gmail for smarter sales follow-ups.
Recommendation system projects using Knowledge, Content, and Collaborative based.
Audiovisual content discovery and personalized recommendations application
An RESTful API paired with a content scraper that analyzes popular YouTube content and arranges it in interesting ways for the end user (via API endpoints).
🎲 Explore demo files in this randomly created repository inspired by Netflix, showcasing creative solutions and engaging project ideas.
🎬 Explore a Netflix-inspired collection of creative projects, offering randomness and enjoyment for entertainment enthusiasts.
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