🤔 ✍️ Simple user-user recommendation system for gardening supplies to explain collaborative filtering and practice using Spark on Amazon product dataset 🌳 🌿
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Updated
Apr 25, 2017 - Jupyter Notebook
🤔 ✍️ Simple user-user recommendation system for gardening supplies to explain collaborative filtering and practice using Spark on Amazon product dataset 🌳 🌿
Package provides java implementation of big-data recommend-er using Apache Spark
Simple Content based and Collaborative Filtering Algorithms implementaion
Implementation of Recommender Systems (RS) using Apache Spark MLlib on movielens dataset
initial fork from
Using yelp data, we have built a restaurant recommendation system for individuals and organizations. For each of the Yelp users, we create a ALS model to recommend restaurants to user using users’ preference and previous user selected data. We propose a solution that would recommend restaurant to users and perform sentiment analysis on restauran…
Recommend Restaurants to User based on the ratings given by them to the restaurants
Yet Another Recommender System Tools
Recommender System (Java, Apache Spark)
A streaming BSGD ALS implementation for Apache Spark
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 Jupyter Notebook outlines my process as I create a movie recommendation system using matrix factorization. I use the public 100k MovieLens dataset.
Recommend movies based on users' ratings, users' features and movie features
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.
Computational Intelligence Lab project at ETH Zurich.
A pure Python implementation of Alternating Least Squares (ALS)
openmp examples
Worked on three use cases- Churn data analysis, Movie recommendation engine and Intrusion detection system.
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