Recomender Systems
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Imagine a world where we didn’t have to go looking for what we wanted to see (in other words, where we were able to "discover" new things that we didn’t yet know we wanted to see, but end up loving the new things and learning or gaining a new interest). Such is the premise of the OpenRecommender project , suggesting a recommendation-based solution to the growing information problem. In order to accomplish this vision, we will be releasing a number of technologies and related data exchange formats which we hope the open source developer community will incorporate into their existing applications, whenever they have a need to work with or serve up recommendations to their users.
OpenRecommender | What will you share today?
Apache Mahout: Scalable machine learning and data mining
Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms Scalable to support your business case.
The Duine Framework is a (collection of) software libraries that allows developers to create prediction engines for their own applications. A prediction engine is a component that predicts how interested individual users are in pieces of information. Such predictions can be used to personalise information to users, specifically in recommending to users what information is and is not of interest to them. Duine is the Irish Gaelic word for person and is pronounced as “dinne” (diner without an ‘r’).
Duine Framework - Recommender Software Toolkit
Cofi: A Java-Based Collaborative Filtering Library
Collaborative filtering is the process of predicting ratings based on a database of ratings from various users. It is widely applicable to e-Commerce, e-Learning, and so on. Currently, programmers who want to use collaborative filtering have to read the literature and implement their own algorithms. More often than not, programmers probably design their own algorithms and they will generally produce suboptimal algorithms. We want to build a foundation of already tested algorithms and documented that can be used in a wide range of contexts from research to applications.
Current version: 1.0, 11/15/2000 SUGGEST is a Top - N recommendation engine that implements a variety of recommendation algorithms. Top- N recommender systems, a personalized information filtering technology, are used to identify a set of N items that will be of interest to a certain user.
SUGGEST: Recommendation Engine | Karypis Lab
qizmt - MySpace Qizmt - MySpace’s Open Source Mapreduce Framework - Google Project Hosting
MySpace Qizmt is a mapreduce framework for executing and developing distributed computation applications on large clusters of Windows servers. The MySpace Qizmt project develops open-source software for reliable, scalable, super-easy, distributed computation software.
Evaluate We are hosting a public evaluation instance of easyrec. Feel free to integrate recommendations with this service into your applications.