OpenRecommender v1.0 launches

This post is to announce the general availability of OpenRecommender version 1.0 to downloading, cloning/forking on Git and testing as Windows or Unix binaries from SourceForge.

Which type of algorithm do you believe gives the best Recommendations for video content on the web?

Collaborative Filter (User-based)
21% (10 votes)
Collaborative Filter (Item-based)
31% (15 votes)
Deep/Categorical Randomization
2% (1 vote)
Popularity Ranking
6% (3 votes)
String/Pattern Matching
4% (2 votes)
Social Network Analysis
8% (4 votes)
Machine Learning
21% (10 votes)
Description Logic/Rules
6% (3 votes)
Total votes: 48

Re-Launch of OpenRecommender Project Site in DRUPAL 7

Welcome to the new home of OpenRecommender, an initiative to create the world's leading open source Recommendation Engine.

Recommendation layout samples

The first demo of the Recommendation Engine widget is available as a preview here:
Recommendations Widget

Recommendation data format (FINAL)

FINAL DRAFT

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.

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