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.