International Journal of Computational Linguistics Research Najma Hamzaoui, Abdelfettah Sedqui, Abdelouahid Lyhyaoui 3 3 2012 http://www.dline.info/jcl/fulltext/v3n3/2.pdf Collaborative filtering algorithm (CF) is a personalized recommendation algorithm that is the most widely used in e-commerce. CF still needs to be improved so that it can make adequate recommendations and solve the problems such as scalability, smoothing the rating estimation. In this paper, we provide an approach of an item based collaborative filtering using item clustering prediction and including a new enhanced correlation similarity. Firstly, we cluster the items i n some groups. Then, in the process of collaborative filtering recommendation, we need to calculate the similarity between the targeted item and items in the selected center. For this aim, an enhanced similarity measure based on multi criteria is proposed instead of the similarity based just on ratings’ items. The objective is to consider when we calculate similarity, the integration of item rating information, the background of the item and the time-weight as criteria of the item evaluation into a convex model. In so doing, the amelioration of the similarity between items performs the recommendation. This proposed CF algorithm is showing to reduce also the influence of the former evaluation of the item.