A website for recommending books
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Updated
Dec 17, 2019 - Python
A website for recommending books
Perform Sparse Matrix Factorization using GPU in CUDA
Compressing RGB images using SVD matrix decomposition
Global optimization for mixed membership matrix factorization for omics data. Code for Zhang, et al, 2019
Applied linear Algebra Projects -Spring 2022
Hybrid (content-based/collaborative) recommendation model to find the best movie for two people to watch together.
A recommender system using matrix factorization algorithm and MovieLens dataset.
Simple and user-friendly Python package for building recommendation systems based on PMF.
Load Tensor Decompositions results
Use the W-ALS algorithm described in Yifan et. al (2008) to build recommendations to users for artists in the LastFM dataset
Implementation of different approaches to recommendation on Amazon Review dataset
Recommender System 2019 Challenge PoliMi
A Machine Learning project that predicts how much a user would enjoy a movie based on its previous ratings given to watched movies.
Coursework to create a recommender system using a variant of matrix factorisation
Built a Recommender System is a system that seeks to predict or filter preferences according to the user’s choices.
Recommend books using various machine learning algorithms.
Web Application Technologies course project
Performed EDA, created user-article matrix, calculated similarity using dot product, implemented Rank-Based, User-User CF, Content-Based, and Matrix Factorization, evaluated model with precision, recall, and F1-score.
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
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