Implementation of Data Mining Algorithm on Spark with Python3
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Updated
May 14, 2020 - Python
Implementation of Data Mining Algorithm on Spark with Python3
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.
Collaborative filtering books recommender system
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
Book recommender api written in flask framework
Semantic search with embeddings: index anything
Django SDK for the Very Easy AI Recommendation engine
Research work related to producing NFT (Non-fungible Token) Recommendations.
The objective of this project is to build a recommendation system to recommend movies to users based on the ratings given to different movies by the users.
A collection of research on knowledge graphs
Various recommendation approaches on IBM Watson platform
Notebooks on using transformers for sequential recommendation tasks
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! ;)
A recommendation engine for the clever. Caidin is a Python library that empowers developers to build smart recommendation systems, including content-based and collaborative filtering methods, making personalized recommendations a breeze.
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.
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.
This repository contains code and analysis for a homework assignment on recommendation systems and clustering algorithms in Python. Implements techniques like minhash, LSH, feature engineering, dimensionality reduction, K-means and DBSCAN clustering.
🟣 Recommendation Systems interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.
Datasets and code used for Scientific Article Recommendations
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.
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