Simple Content based and Collaborative Filtering Algorithms implementaion
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Updated
Nov 10, 2017 - Python
Simple Content based and Collaborative Filtering Algorithms implementaion
Implementation of Recommender Systems (RS) using Apache Spark MLlib on movielens dataset
Yet Another Recommender System Tools
There are Python 2.7 codes and learning notes for Spark 2.1.1
A set of matrix factorization techniques to provide recommendations for implicit feedback datasets.
This is a repository containing a copy of a project I made for a course from NYU. It contains code and a report describing a modification of the matrix factorization method Alternating Least squares.
A pure Python implementation of Alternating Least Squares (ALS)
An anime recommendation engine that allows us to recommend anime based on a given anime title or a given user using Pyspark
A python program that applies a choice of nonnegative matrix factorization (NMF) algorithms to a dataset for clustering.
Recommendation System using MLlib and ML libraries on Pyspark
🎵 Utilized the Spark engine to build and evaluate a music recommender system and accelerated query search from utilizing spatial data structure by using the Annoy
Recommender System 2019 Challenge PoliMi
Recommendation system using alternating least squares method
Scalable Book Recommender System - Apache Spark ML Lib
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
Matrix factorization for collaborative filtering using ALS specifically optimized for rank-1 approximation.
The objective of the competition was to create the best recommender system for a book recommendation service by providing 10 recommended books to each user. The evaluation metric was MAP@10.
In this project, a Recommender System is built from 2 popular methods which are Content Based Filtering (Gensim and Cosine Similarity algorithms) and Collaborative Based Filtering (Alternating Least Square model in PySpark). Then, this recommender system is deployed onto Heroku cloud platform.
This project looked at scalable machine learning for a movie recomemdantion set utilising ALS (alternative least squares) A NASA data set was used as a familiarisation to pySpark. The data was plotted using matplotlib.
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