A collection of research on knowledge graphs

Semantic search with embeddings: index anything

Course on recommender systems conducted at the Faculty of Computer Science, National Research University - Higher School of Economics. Academic year 2022-2023.

MovieGPT: A RAG, Gen AI application for Movie Recommendations

Notebooks on using transformers for sequential recommendation tasks

Regression-based Movie Recommender system that's a hybrid of content-based and collaborative filtering Recommender system Simply rate some movies and get immediate recommendations tailored for you

🟣 Recommendation Systems interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.

Research work related to producing NFT (Non-fungible Token) Recommendations.

List of all ML projects

Collaborative filtering books recommender system

This repository comprises of the projects and assignments that i have completed during my tenure at Great Lakes for the course program PGP-AIML. This repository also includes the lab work thatwas done during the classes and even those that were given as assessments.

The Movie Recommendation System is an advanced machine learning project developed in Python, aimed at providing tailored movie suggestions to users based on their preferences and viewing habits. Leveraging various machine learning algorithms and data processing techniques, this system offers a personalized and enriched movie-watching experience.

Discover the Machine learning datasets! Diverse content for 🎓 education, 📊 research, 👥 non-profit use and experimenting. Download, merge files for 📝 convenience. Contribute to enhance language modeling, 🤖 machine learning, 🎓 education, data analysis, and 🧪 software development. Note: Content sourced for non-profit, educational use. Enjoy! ;)

Book recommender api written in flask framework

Personal project in which I analyse the data of an E-Commerce business.

Django SDK for the Very Easy AI Recommendation engine

The repository prompts the user to select the recommendation approach, user-based (correlation). Based on the selected approach and similarity metric, this function predicts the rating for specified user and item and also suggests if the item could be recommended to the user.

This is the material for Jose Portilla's Spark and Python for Big Data and ML course.

Recommendation Systems course at AGH UST 2023/2024. This repository is packed with Jupyter Notebook files, written in Python, to guide you through the theory and implementation of recommendation algorithms.