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Running Mahout in the Cloud using Apache Whirr

This blog shows you how to run Mahout in the cloud, using Apache Whirr. Apache Whirr is a promosing Apache incubator project for quickly launching cloud instances, from Hadoop to Cassandra, Hbase, Zookeeper and so on. I will show you how to setup a Hadoop cluster and run Mahout jobs both via the command line […]

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Mahout – Taste :: Part Three – Estimators

In Taste, estimators are the bridge between the generic item- or user recommendation logic and the specific similarity algorithm. Estimators are mainly used as part of the recommendation process, however, they are also used for evaluating recommenders. Additionally, the ‘recommended because’ feature is also powered by an estimator. This blog covers some Taste internals and […]

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Mahout – Taste at Lucene Eurocon and Berlin Buzzwords

A little while ago, I was delighted to present two introductory Mahout – Taste talks, at Lucene Eurocon and Berlin Buzzwords. I received quite a lot of good feedback about the presentations and have been asked by a few attendees to post them. If you’re one of those attendees or you missed the presentation, you […]

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Mahout – Taste :: Part Two – Getting started

This blog is a ‘getting started’ article and shows you how to build a simple web-based movie recommender with Mahout / Taste, Wicket and the Movielens dataset from Grouplens research group at the University of Minnesota. I will discuss which components you need, how to wire them up in Spring, and how to create a […]

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Mahout – Taste :: Part 1 – Introduction

Mahout – Taste :: Part 1 – Introduction

This post is the first in a series on Taste, a Java framework for providing personalized recommendations. Taste is part of the larger Mahout framework, which features various scalable machine-learning algorithms. In this post I introduce you to the concepts of personalized recommendations, also known as collaborative filtering. After this introduction, Taste’s architecture and extension […]

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