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
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.
A modern, responsive web application that delivers personalized content recommendations based on user preferences and behavior. This interactive recommendation system allows users to discover content tailored to their interests through category selection, tag filtering, and customizable content parameters.
Recommendation System & it's types
A trust based social network for user engagement and protection.
Content, Collaborative, and Hybrid Movie recommendation system
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).
Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback. I have applied basic content-based recommendation system using python.
Recommendation system projects using Knowledge, Content, and Collaborative based.
Aplicación de descubrimiento y recomendaciones personalizadas de contenido audiovisual
Interactive Power BI dashboard analyzing Netflix titles, viewing patterns, and strategic recommendations.
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