A Parallel Recommendation Engine in Julia – Julia Computing
Recommender systems play a pivotal role in various business settings like e-commerce sites, social media platforms, and other platforms involving user interaction with other users or products. Recommender systems provide valuable insights to gain actionable intelligence on these users. Large Scale Recommender systems help in unraveling the latent information in the complex relational data between
Accelerate Recommender Systems with GPUs | NVIDIA Technical Blog
Wei Tan's team at IBM T. J. Watson Research Center developed cuMF, a CUDA-based matrix factorization library that optimizes the Alternating Least Squares (ALS) method to solve large-scale matrix factorization problems using NVIDIA GPUs.cuMF achieves excellent scalability and performance by applying techniques such as memory access optimization, data parallelism, and a topology-aware parallel reduc
How to recommend top 10 products in Spark ALS for all the users?
I wrote this function which multiplies user features and product features by partitions so that it gets distributed then it gets the ratings for each product by user and sorts them by rating and outputs the list of 8 top recommended products. #Collect product feature matrix productFeatures = bestModel.productFeatures().collect() productArray=[] productFeaturesArray=[] for x in productFeatures: pro
highdimensionality.com - highdimensionality リソースおよび情報
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How to Build a Recommendation Engine on Spark
The document outlines the construction of a recommendation engine using Spark atop Hadoop to enhance customer experience and retention in various industries. It discusses the role of big data analytics in creating relevant product suggestions through content-based and collaborative filtering methods, alongside the mathematical computations involved in determining item similarities. The ultimate go
Spark の MLlib を使った Dataproc でのレコメンドシステムの実装 - Qiita
はじめに 前回の Advent Calendar の投稿では, 簡単に Dataproc の特徴について説明を行いましたが、今回は, 実際に Dataproc を使ったレコメンドシステムの実装について解説したいと思います。 実装するシステムの概要 【追記】 下記では、BigQuery から直接読み込む実装にしていますが、データ量が大きい場合（数百MB以上）は、テキストデータに比べて読み込みに大幅に時間がかかるため、BigQuery から GCS へ一旦エクスポートし、CSVファイルとして読み込むことをお勧めします。 本投稿では、ユーザーのアイテムに対するLikeや閲覧履歴から、ユーザーに対してアイテムをレコメンドするシステムを実装します。ライブラリとしては、Matrix Factorization を Alternating Least Square という手法で計算するライブラリを使って
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[SPARK-6334] spark-local dir not getting cleared during ALS - ASF JIRA
https://www.codementor.io/spark/tutorial/building-a-web-service-with-apache-spark-flask-example-app-part2