集成社会化标签和用户背景信息的协同过滤推荐方法  

Collaborative filtering recommendation method of integrating social tags and users background information

作　　者：蒋胜[1] 王忠群[1] 修宇[1] 皇苏斌[1] 

机构地区：[1]安徽工程大学计算机与信息学院,安徽芜湖241000

出　　处：《计算机应用》2014年第8期2328-2331,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(71371012;71171002);教育部人文社科规划项目(13YJA630098)

摘　　要：针对传统的协同推荐算法存在数据稀疏和推荐精度低的问题,提出了一种集成社会化标签和用户背景信息的协同过滤(CF)推荐方法。首先,分别计算基于社会化标签和用户背景信息的用户间的相似度;然后,基于用户评分计算用户间的相似度;最后,集成上述3种相似性度量产生用户间综合相似度,并对目标用户进行项目推荐。实验结果表明,与传统的协同过滤推荐算法相比,所提方法在正常数据集和冷启动数据集下的平均绝对误差(MAE)平均降低了16%和22.6%。该方法不仅能有效地提高推荐算法的精度,而且能较好地解决数据稀疏和冷启动的问题。To address the difficulty of data sparsity and lower recommendation precision in the traditional Collaborative Filtering （CF） recommendation algorithm, a new CF recommendation method of integrating social tags and users background information was proposed in this paper. Firstly, the similarities of different social tags and different users background information were calculated respectively. Secondly, the similarities of different users ratings were calculated. Finally, these three similarities were integrated to generate the integrated similarity between users and undertook the recommendations about items for target users. The experimental results show that, compared with the traditional CF recommendation algorithm, the Mean Absolute Error （MAE） of the proposed algorithm respectively reduces by 16% and 22.6% in the normal dataset and cold-start dataset. The new method can not only improve the accuracy of recommendation algorithm, but also solve the problems of data sparsity and cold-start.

关 键 词：数据稀疏 冷启动 用户背景信息 社会化标签 协同过滤推荐 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]