
Recomender Systems
Get flash to fully experience Pearltrees
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.Duine Framework - Recommender Software Toolkit
Cofi: A Java-Based Collaborative Filtering Library
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.

