Notice of RetractionCollaborative filtering based on time division

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Collaborative filtering techniques are widely used in e-commerce systems. However, the rating data are very sparse, which affects prediction accuracy greatly. A time division based collaborative filtering algorithm is proposed in this paper. According to the current time and the released time of items, items are divided into three types, named new item, normal item and old item respectively. Accordingly, users´ interests are calculated in the three types respectively and user-item matrix is expanded. When predicting the rate of an item for a user, different strategies are used with different types of items. Data sparsity and new item problems are solved effectively. Empirical studies on MovieLens have shown that our newly proposed method improve recommendation quality obviously.