A very primitive type of recommender system
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Updated
Sep 17, 2015 - Shell
A very primitive type of recommender system
Assignements of various SQL courses (Introduction to Data Science; Stanford Database course)
Making movies recommendation using a Collaborative Filtering Algorithm on the famous MovieLens dataset.
Label consistent RBM
Created a Recommender system based on Products of Factors technique
Recommender System(s)
Applied Extreme Learning Machine (ELM) to the domain of Collaborative Filtering.
Collaborative Filtering for MovieLens data
Implemented sparse matrix completion algorithms and principles of recommender system to develop a predictive user-movie rating model.
movie recommendation system implemented by jupyter notebook
Recommender System based on Collaborative Filtering using CoffeeScript
Recommender System based on Collaborative Filtering using Ruby
Map/Reduce application that analyzes movie ratings collected by Movielens, leveraging Hadoop MapReduce, Hadoop Distributed File System and Apache Flume. Coursework in Structures and Architectures for Big Data 2016/2017.
New algorithms for Large-scale Collaborative Ranking: PrimalCR and PrimalCR++
Movie Recommender based on the MovieLens Dataset (ml-100k) using item-item collaborative filtering.
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