基于项目评分预测的协同过滤推荐算法

A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction

推荐系统是电子商务系统中最重要的技术之一.随着电子商务系统用户数目和商品数目的日益增加,在整个商品空间上用户评分数据极端稀疏,传统的相似性度量方法均存在各自的弊端,导致推荐系统的推荐质量急剧下降.针对用户评分数据极端稀疏情况下传统相似性度量方法的不足,提出了一种基于项目评分预测的协同过滤推荐算法,根据项目之间的相似性初步预测用户对未评分项目的评分,在此基础上,采用一种新颖的相似性度量方法计算目标用户的最近邻居.实验结果表明,该算法可以有效地解决用户评分数据极端稀疏情况下 传统相似性度量方法存在的问题,显著地提高推荐系统的推荐质量.

Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.