Movie Recommendation System using the 10M MovieLens dataset
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Updated
Feb 8, 2022 - R
Movie Recommendation System using the 10M MovieLens dataset
Spring2020-Project4-spring2020-project4-group7 created by GitHub Classroom
Movie recommendation system using the 10 million MovieLens dataset.
This R package is used for generating automatic recommendations with association rule learning, using mined association rules from the arules package.
Fixed-volume neighborhood classifier with binary feedback
implementation of recommender system on R include:UBCF,Slope One,SVD//毕业论文测试
Web app using spotify API. Three main features are recommending music, visualising lyrics and visualising collaboration networks.
This project focuses on exploring product recommendations within the realm of luxury fashion resale. It serves as a companion to my thesis of the MSc Marketing Analytics program at TiU.
IMDB movies recommender system
Movie Recommendations code using R/Knit. Made use of RecommenderLab package.
Building a recommendation system of movies based on user rating
A simple content-based recommender system
A rudimentary script for tracking and selecting stocks to invest in
Product Recommentation using SVD
Recommendation systems are well-known machine learning systems that use data to predict and provide suggestions for an item or items in such a way that users can choose it from a huge number of items offered to them.
Project on recommender systems within the data mining course in my master's program.
In this project, the MovieLens 10M dataset was used to create a movie recommendation system algorithm that can be used to predict how a certain user will rate a certain movie.
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