考虑物品相似权重的用户相似度计算方法

User similarity function considering weight of items similarity

传统的用户相似度计算方法中每个项目的权重是相同的,然而分析传统推荐算法和现实情形,用户间共同高评分项目的权重应该高于用户间共同低评分项目的权重,并且传统用户相似度计算方法没有考虑项目间的类群关系。针对上述问题,提出了一种给项目加权的方法,从而得到考虑项目相似权重的用户相似度计算方法。通过在Movie Lens数据集上进行实验,与基于传统用户相似度计算方法的协同过滤算法比较,实验结果表明,考虑了项目相似度权重的协同过滤算法能显著提高评分预测的准确性和推荐系统的质量。

In traditional user similarity function, the weight for each item is the same. However, analyzing traditional col-laborative filtering algorithms and practical case, the weight of user’s jointly high scoring item should be higher than the weight of user’s jointly low scoring item. And traditional user similarity function does not take taxa relationship between the items. To address the problem, it proposes a method to weight project and finally obtains a user similarity function which considers the similarity weight of items. The experimental results conducted on the movieLens data sets show that compared with the collaborative filtering algorithm which is based on the traditional user similarity function, the collabor-ative filtering algorithm which considers the similarity weight of items can significantly improve the ratings prediction accuracy and the quality of the recommendation system.