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

A trust based social network for user engagement and protection.

Interactive Power BI dashboard analyzing Netflix titles, viewing patterns, and strategic recommendations.

A metadata-driven movie similarity engine built from The Movies Dataset on Kaggle. The system preprocesses raw metadata, stores it in SQLite, generates sparse feature vectors, and computes similarity rankings using cosine similarity. A FastAPI backend serves results through a UI and API layer, with the full application deployed on Render.

Content, Collaborative, and Hybrid Movie recommendation system

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).

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.

🎬 Explore a Netflix-inspired collection of creative projects, offering randomness and enjoyment for entertainment enthusiasts.

🎲 Explore demo files in this randomly created repository inspired by Netflix, showcasing creative solutions and engaging project ideas.