document: Clustering for probabilistic model estimation for CF Based on the type of collaborative objects , a collaborative filtering ( CF ) system falls into one of two categories : item-based CF and user-based CF. Clustering is the basic idea in both cases , where users or items are classified into user groups where users share similar preference or item groups where items have similar attributes or characteristics . Observing the fact that in user-based CF each user community is characterized by a Gaussian distribution on the ratings for each item and the fact that in item-based CF the ratings of each user in item community satisfy a Gaussian distribution , we propose a method of probabilistic model estimation for CF , where objects ( user or items ) are classified into groups based on the content information and ratings at the same time and predictions are made considering the Gaussian distribution of ratings . Experiments on a real-world data set illustrate that our approach is favorable .
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