Improvement of Item-Based Collaborative Filtering Algorithm
Wang Zhu-wei · 2010
This main objective of this study is to improve the item-based collaborative filtering algorithm.The traditional item-based collaborative filtering algorithm faced the double troubles on recommendation inefficiency and low accuracy.Therefore,the Weighted integration of clustering classification forecasting method is proposed in this paper.The method uses the re-weighted scoring approach and the re-definition of similarity approach in the data processing and in the process of forecasting recommendation respectively.It uses item clustering method to improve the recommendation efficiency.Meanwhile,the concept of contribution degree parameters to revise the data processing and the prediction of recommendation process is proposed.Through the comparison of the experiment with Movielens data sets,the results show that the improved algorithm can significantly improve the efficiency and accuracy of the collaborative filtering.,and also can maintain a low average absolute deviation and higher level of efficiency of the recommendation even when the data is relatively sparse.