Collaborative filtering recommendation algorithm based on semantic similarity of item

The accuracy and quality is the best evaluation of recommend system. This paper proposes a collaborative filtering remmendation algorithms based on computing the sematic similarity of items in order to improve the accuracy of items' similarity. The experimental results shows that the optimized algorithm can give a better prediction, by way of increasing accuracy and reducing cold-start problem of item.