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Model-based collaborative filtering algorithms provide item
recommendation by first developing a model of user
ratings. Algorithms in this category take a probabilistic approach
and envision the collaborative filtering process as computing the
expected value of a user prediction, given his/her ratings on other
items. The model building process is performed by different *machine learning*
algorithms such as **Bayesian network, clustering,** and **rule-based** approaches. The Bayesian network model [6] formulates a
probabilistic model for collaborative filtering problem. The clustering
model treats collaborative filtering as a classification
problem [2,6,29] and works by
clustering similar users in same class and estimating the probability
that a particular user is in a particular class *C*, and from there
computes the conditional probability of ratings. The rule-based
approach applies association rule
discovery algorithms to find association between co-purchased items
and then generates item recommendation based on the strength of the
association between items [25].

*Badrul M. Sarwar*

*2001-02-19*