Using PySpark Mlib and ALS model to create book recommendation

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

NLP + Classical ML projects(Regression analysis, Classification models etc)

Movies suggestions using ALS (inspired Netflix Challenge)

A MATLAB implementation of "Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares".

SereneSounds is a sophisticated music curation web app that leverages various recommendation techniques to provide users with diverse and personalized music suggestions.

ETL pipeline and Machine Learning model for recommending cryptocurrencies

Machine Learning, Python

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.

This repository includes a web application that is connected to a product recommendation system developed with the comprehensive Amazon Review Data (2018) dataset, consisting of nearly 233.1 million records and occupying approximately 128 gigabytes (GB) of data storage, using MongoDB, PySpark, and Apache Kafka.

Using Spark to analyze a dataset, public or self-collected, and drawing some insights from it

A collaborative-filter-based music recommender machine

Recommender systems on MovieLens data using explicit ratings, and curated implicit feedback data.

This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to…

🎓 Final Project for Completing Bachelor Degree in Petra Christian University. Create Hybrid Recommender System for Interior Products and its Services using Data Implicit Feedback

Collaborative-filtering Recommender System using Spark Alternating Least Squares method

🎥👨‍🔬 Big Data Final Project to create Recommendation System using Alternating Least Squares. This Recommendation uses explicit data such as rating as input to methods

Recommender system in retail

Training an ALS (Alternating Least Squares) recommendation model on the MovieLens 100k dataset

Homework repository for the 2022/23 course Mining massive datasets @FINKI