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Probabilistic models for data combination in recommender systems

By Sinead Williamson and Zoubin Ghahramani


In a typical collaborative filtering problem, the dataset is an incomplete matrix of ratings R given by a set U of users to a set I of items, and the task is to predict what ratings the users would give to the items they have not yet rated. A common approach to this problem is to use matrix factorization techniques to find a lower dimensional representation, R ≈ UM

Year: 2008
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