Collaborative Filtering Recommendation Based on Content and Item Rating Prediction
Yan Zeng · Jisuanji gongcheng · 2004
Traditional similarity measure methods work poor in this situation,which makes the quality of recommendation system decrease dramatically. To address this issue a novel collaborative filtering algorithm based on content and item rating prediction is proposed. This method predicts item ratings that usesr have not rated based on content prediction and then uses item-based collaborative filtering to find similar items and make a prediction. The experiment results suggeste that this method can efficiently improve the extreme sparsity of user rating data,improve accuracy of recommendation using item-based collaborative filtering,and provide better recommendation results than nearest neighborhood collaborative filtering algorithms.