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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 .
Badrul M. Sarwar