There are Python 2.7 codes and learning notes for Spark 2.1.1
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
A python program that applies a choice of nonnegative matrix factorization (NMF) algorithms to a dataset for clustering.
A set of matrix factorization techniques to provide recommendations for implicit feedback datasets.
Recommendation System using MLlib and ML libraries on Pyspark
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.
A pure Python implementation of Alternating Least Squares (ALS)
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.
Recommendation system using alternating least squares method
Yet Another Recommender System Tools
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
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.
🎵 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
Full stack machine learning music recommendation app using ALS collaborative filtering, built using Flask and PySpark
Collaborative-filtering Recommender System using Spark Alternating Least Squares method
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.
Recommender systems on MovieLens data using explicit ratings, and curated implicit feedback data.
A movie recommendation system on MovieLens 25M dataset using Python and Apache Spark
🎓 Final Project for Completing Bachelor Degree in Petra Christian University. Create Hybrid Recommender System for Interior Products and its Services using Data Implicit Feedback
An anime recommendation engine that allows us to recommend anime based on a given anime title or a given user using Pyspark
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