Collaborative filtering recommendation based on item rating and characteristic information prediction
Mingjia Wang, Jin-Ti Han · 2012
To deal with the sparsity and expansibility of traditional collaborative filtering algorithm, a collaborative filtering algorithm based on item rating was proposed in this paper. The method can calculation the item ratings of project that have not rated based on the analysis of the item characteristic information, and use item-based collaborative filtering algorithm to find the similar items. Moreover, the paper puts forward a new formula to compute the rating values of the item that users have not rated. The experiment results demonstrate that the new algorithm could improve the accuracy of recommendation under the condition of the extreme sparsity of user rating data.