The Matchbox system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behavior in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped into a low-dimensional ‘trait space’ in which similarity is measured in terms of inner products. The model can be trained from different types of feedback in order to learn user-item preferences.
To find out more about this system, visit Microsoft's website at:
http://research.microsoft.com/apps/pubs ... x?id=79460
Anything related to BNs, including models, software and theory
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