Probabilistic Models for Data Combination in Recommender Systems
published: Dec. 20, 2008, recorded: December 2008, views: 9292
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We propose a method for jointly learning multiple related matrices, and show that, by sharing information between the two matrices, such an approach allows us to improve predictive performances for items where one of the matrices contains very sparse, or no, information. While the above justification has focused on recommender systems, the approach described is applicable to any two datasets that relate to a common set of items and can be represented in matrix form. Examples of such problems could include image data where each image is associated with a set of words (for example captioned or tagged images); sets of scientific papers that can be represented either using a bag-of-words representation or in terms of their citation links to and from other papers; corpora of documents that exist in two languages.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !