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

doi:10.11772/j.issn.1001-9081.2014.08.2328

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (8): 2328-2331.DOI: 10.11772/j.issn.1001-9081.2014.08.2328

• Artificial intelligence • Previous Articles Next Articles

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

JIANG Sheng,WANG Zhong-qun,XIU Yu,HUANG Subin

School of Computer and Information, Anhui Polytechnic University, Wuhu Anhui 241000, China

Received:2014-03-06 Revised:2014-04-22 Online:2014-08-10 Published:2014-08-01
Contact: JIANG Sheng
Supported by:
Project supported by the National Natural Science Foundation of China

集成社会化标签和用户背景信息的协同过滤推荐方法

蒋胜,王忠群,修宇,皇苏斌

安徽工程大学计算机与信息学院，安徽芜湖241000

通讯作者: 蒋胜
作者简介:蒋胜（1991-），男，安徽滁州人，硕士研究生，主要研究方向：电子商务；王忠群（1965-），男，安徽芜湖人，教授，主要研究方向：信息管理与信息系统、软件工程、工作流；修宇（1976-），男，安徽芜湖人，讲师，主要研究方向：数据挖掘、机器学习。
基金资助:
国家自然科学基金资助项目;教育部人文社科规划项目

Abstract

Abstract:

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.

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。

CLC Number:

TP391
TP18

JIANG Sheng WANG Zhong-qun XIU Yu HUANG Subin. Collaborative filtering recommendation method of integrating social tags and users background information[J]. Journal of Computer Applications, 2014, 34(8): 2328-2331.

蒋胜王忠群修宇皇苏斌. 集成社会化标签和用户背景信息的协同过滤推荐方法[J]. 计算机应用, 2014, 34(8): 2328-2331.

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

集成社会化标签和用户背景信息的协同过滤推荐方法

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