Powered By Apache Mahout

365Media used Mahout's classification and collaborative filtering algorithms in its real-time system UPTIME and in 365Media/Social.

Accenture used Mahout as the typical example in its Hadoop Deployment Comparison Study.

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Adobe AMP used Mahout's clustering algorithms to increase video consumption through better user targeting.

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Amazon's Personalization Platform used Mahout.

AOL used Mahout for shopping recommendations.

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Booz Allen Hamilton used Mahout's clustering algorithms for large-scale biometric analysis.

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Buzzlogic used Mahout's clustering algorithms to improve ad targeting.

Cull.tv used modified Mahout algorithms for content recommendations.

DataMine Lab used Mahout's recommendation and clustering algorithms to improve its clients' ad targeting.

The Drupal Recommender API module used Mahout to provide open source content recommendation for Drupal sites.

Evolv used Mahout for its Workforce Predictive Analytics platform.

Foursquare used Mahout for its venue recommendation engine.

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Idealo used Mahout's recommendation engine.

InfoGlutton used Mahout's clustering and classification for various consulting projects.

Intel shipped Mahout as part of its Distribution for Apache Hadoop Software.

Intela implemented Mahout's recommendation algorithms to select new offers to send to customers and to recommend potential customers for current offers, and used the clustering algorithms to enhance its offer categories.

iOffer used Mahout's frequent pattern mining and collaborative filtering to recommend items to users.

Kauli, a Japanese ad network, used Mahout's clustering to handle clickstream data for predicting audience interests and intents.

LinkedIn experimented with Mahout for model training as an alternative to R-based workflows.

LucidWorks Big Data used Mahout for clustering, duplicate document detection, phrase extraction, and classification.

Mendeley used Mahout to power Mendeley Suggest, a research article recommendation service.

Mippin used Mahout's collaborative filtering engine to recommend news feeds.

Mobage used Mahout in its analysis pipeline.

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Myrrix was a recommender system product built on Mahout.

NewsCred used Mahout to cluster news articles and surface the important stories of the day.

Next Glass used Mahout.

Predixion Software used Mahout's algorithms to build predictive models on big data.

Radoop provided a drag-and-drop interface for big data analytics, including Mahout clustering and classification algorithms.

ResearchGate, the professional network for scientists and researchers, used Mahout's recommendation algorithms.

Sematext used Mahout for its recommendation engine.

SpeedDate.com used Mahout's collaborative filtering engine to recommend member profiles.

Twitter used Mahout's LDA implementation for user interest modeling, and maintained a fork of Mahout on GitHub that was periodically synced with Apache trunk.

Yahoo! Mail used Mahout's frequent pattern set mining in its anti-spam pipeline.

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Carnegie Mellon University used Mahout as a comparison platform in GraphLab research.

Researchers at the Digital Enterprise Research Institute used Mahout for topic mining and modelling of large corpora.

The Dicode project used Mahout's clustering and classification algorithms on top of HBase.

Nagoya Institute of Technology used Mahout for research and data processing in a large-scale citizen participation platform funded by the Ministry of Interior of Japan.

The NoTube EU project used Mahout's Taste recommender in its applications.

The ROBUST project, co-funded by the European Commission, employed Mahout in the large-scale analysis of online community data.

The Large Scale Data Analysis and Data Mining course at TU Berlin used Mahout to teach students how to parallelize data mining problems.