There are Python 2.7 codes and learning notes for Spark 2.1.1
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Updated
Aug 21, 2018 - Python
There are Python 2.7 codes and learning notes for Spark 2.1.1
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
Implementation of Recommender Systems (RS) using Apache Spark MLlib on movielens dataset
A python program that applies a choice of nonnegative matrix factorization (NMF) algorithms to a dataset for clustering.
Yet Another Recommender System Tools
Python scripts that implement collaborative filtering using Matrix Factorization with Alternating Least Squares (MF-ALS) for hotels and restaurants, Restricted Boltzmann Machines (RBM) for attractions, and content-based filtering using cosine similarity for the "More Like This" feature.
🎵 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
Recommendation System using MLlib and ML libraries on Pyspark
Recommendation system using alternating least squares method
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.
🎓 Final Project for Completing Bachelor Degree in Petra Christian University. Create Hybrid Recommender System for Interior Products and its Services using Data Implicit Feedback
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.
An anime recommendation engine that allows us to recommend anime based on a given anime title or a given user using Pyspark
Scalable Book Recommender System - Apache Spark ML Lib
Recommender System 2019 Challenge PoliMi
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.
Full stack machine learning music recommendation app using ALS collaborative filtering, built using Flask and PySpark
Recommender System project that uses Weighted Matrix Factorisation to learn user and items embeddings from a (sparse) feedbacks matrix, and uses them to perform user-specific suggestions
Simple Content based and Collaborative Filtering Algorithms implementaion
Movies suggestions using ALS (inspired Netflix Challenge)
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