Many of the recent algorithms have been developed to improve the various aspects of collaborative filtering recommender systems, however, most of them do not take the sectional data of users and items information or characteristic into account. This paper, we present a new improved collaborative filtering based on item similarity modified and item common ratings which take full advantage of the sectional data of item-user matrix information to modify the similarity calculation and rating prediction. Extensive experiments have been conducted on two different dataset to analyze our proposal approach. The results show that our approach can improve the prediction accuracy of the item-based collaborative filtering not only on different neighbors, but also on different training ratio data set.
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