openmp examples
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Updated
Mar 25, 2019 - C++
openmp examples
pyRecLab is a library for quickly testing and prototyping of traditional recommender system methods, such as User KNN, Item KNN and FunkSVD Collaborative Filtering. It is developed and maintained by Gabriel Sepúlveda and Vicente Domínguez, advised by Prof. Denis Parra, all of them in Computer Science Department at PUC Chile, IA Lab and SocVis Lab.
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…
Optimization algorithms for hybrid precoding in mmWave MIMO systems: Version 1.1.0
There are Python 2.7 codes and learning notes for Spark 2.1.1
Recommend Restaurants to User based on the ratings given by them to the restaurants
A movie recommendation system trained on the MovieLens 20 Million dataset. This system makes use of Collaborative filtering methods to come up with recommendations for a particular user.
Collection of basic and advanced Tensor Algebra operations using Matlab and Python.
A python program that applies a choice of nonnegative matrix factorization (NMF) algorithms to a dataset for clustering.
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
Recommender System (Java, Apache Spark)
A set of matrix factorization techniques to provide recommendations for implicit feedback datasets.
Recommender system in retail
Recommendation System using MLlib and ML libraries on Pyspark
A Java implementation of Alternating Least Squares (ALS).
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.
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.
Pairwise Perturbation: an efficient numerical algorithm for alternating least squares in tensor decompositions
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.
A pure Python implementation of Alternating Least Squares (ALS)
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