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.