Collaborative Filtering with Improved Item Prediction Approach for Enhancing the Accuracy of Recommendation

Abstract

Collaborative filtering (CF) is a widely-used technique for generating personalized recommendations. CF systems are typically based on the ratings given by users to items. There are many factors influencing user's rating, beside user's interest and rating scale, item objective character is also the important element. Considering these factors, the improved item prediction approaches present a more rational method to measure user's rating scale, take item objective character into consideration in the processing of prediction. CF with improved prediction approaches are empirically tested in recommendation and shown better recommendation accuracy than traditional CF.