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Subject [CONF] Apache Mahout > Powered By Mahout
Date Sun, 22 Apr 2012 17:31:00 GMT
Space: Apache Mahout (
Page: Powered By Mahout (

Edited by Kris Jack:
h1. Intro
Are you using Mahout to do Machine Learning?  Care to share?

*NOTE: Please add links in alphabetical order.  Links here do NOT imply endorsement by Mahout,
its committers or the Apache Software Foundation and are for informational purposes only.*

h1. Commercial Use

* Adobe AMP uses Mahout's clustering algorithms to increase video consumption by better user
targeting. See []
* Amazon's Personalization Platform -- See []
* [AOL |] use Mahout for shopping recommendations. See []
* [Booz Allen Hamilton |] uses Mahout's clustering algorithms. See
* [Buzzlogic|] uses Mahout's clustering algorithms to improve ad targeting
* [|] uses modified Mahout algorithms for content recommendations
* !! [DataMine Lab|] uses Mahout's
recommendation and clustering algorithms to improve our clients' ad targeting.
* [Drupal|] users Mahout to provide open source content
recommendation solutions.
* [Foursquare|] uses Mahout for its [recommendation engine |].
* [Idealo|] uses Mahout's recommendation engine.
* [InfoGlutton|] uses Mahout's clustering and classification for
various consulting projects.
* [Intela|] has implementations of Mahout's recommendation algorithms
to select new offers to send tu customers, as well as to recommend potential customers to
current offers. We are also working on enhancing our offer categories by using the clustering
algorithms. We have a [blog post|] where
we talk about it. 
* !! [iOffer|] uses Mahout's Frequent Pattern
Mining and Collaborative Filtering to recommend items to users.
* !! [Kauli|], one of Japanese Adnetwork, uses Mahout's
clustering to handle clickstream data for predicting audience's interests and intents.
* !! [Mendeley|] uses Mahout to power Mendeley
Suggest, a research article recommendation service.
* !! [Mippin|] uses Mahout's collaborative filtering
engine to recommend news feeds
* [Mobage|] uses Mahout
in their analysis pipeline
* !! [Myrrix|] is
a recommender system product built on Mahout.
* !! [NewsCred|]
uses Mahout to generate clusters of news articles and to surface the important stories of
the day
* !! [Radoop|] provides a drag-n-drop interface
for big data analytics, including Mahout clustering and classification algorithms
* [Sematext|] uses Mahout for its [Recommendation Engine|]
* [|] uses Mahout's collaborative filtering engine to
recommend member profiles
* [Twitter|] uses Mahout's LDA implementation for user interest modeling,
and maintains a (periodically sync'ed with Apache trunk) [fork|]
of Mahout on GitHub
* [Yahoo\!|] Mail uses Mahout's Frequent Pattern Set Mining.  See []
* !! [365Media |] uses *Mahout's*
Classification and Collaborative Filtering algorithms in its Real-time system named [UPTIME|]
and 365Media/Social

h1. Academic Use

* [Dicode|] project uses Mahout's clustering and classification
algorithms on top of HBase.
* The course [Large Scale Data Analysis and Data Mining|]
at [TU Berlin|] uses Mahout to teach students about the parallelization
of data mining problems with Hadoop and Map/Reduce
* Mahout is used at Carnegie Mellon University, as a comparable platform to [GraphLab|].
* The [ROBUST project|], co-funded by the European Commission,
employs Mahout in the large scale analysis of online community data.
* Mahout is used for research and data processing at [Nagoya Institute of Technology|],
in the context of a large-scale citizen participation platform project, funded by the Ministry
of Interior of Japan.
* Several researches within [Digital Enterprise Research Institute|] [NUI
Galway|] use Mahout for e.g. topic mining and modelling of large corpora.
* We used Mahout in the NoTube EU project, and it saved a lot of time (and a brain transplant).
The only piece we've used heavily in our apps (
so far is the Taste recommender, but I've been digging deeper into the other components. I
can't claim we're a hugely famous or successful application, but I can say without doubt I
don't regret using Mahout. It did what it said it would do, and easily. One nice thing about
this community, is that Mahout is not
over-marketed. If the nature or scale of your problem better suits other tools, the Mahout
folk will tell you so.

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