基于主题的Web文本聚类方法

doi:10.11772/j.issn.1001-9081.2014.11.3140

计算机应用 ›› 2014, Vol. 34 ›› Issue (11): 3140-3143.DOI: 10.11772/j.issn.1001-9081.2014.11.3140

• 2014年全国开放式分布与并行计算学术年会(DPCS 2014)论文 • 上一篇下一篇

基于主题的Web文本聚类方法

王雪霞,李青,李季红

上海大学计算机工程与科学学院,上海 200444

收稿日期:2014-07-16 修回日期:2014-07-30 发布日期:2014-12-01 出版日期:2014-11-01
通讯作者: 王雪霞
作者简介:王雪霞(1988-),女,山东烟台人,硕士研究生,主要研究方向:推荐系统、复杂网络;李青(1962-),男,湖北嘉鱼人,教授,博士生导师,CCF会员,主要研究方向:并行计算、复杂网络、复杂系统建模、计算机模拟;李季红(1987-),男,浙江杭州人,硕士,主要研究方向:并行计算、推荐系统。

Collaborative filtering recommendation based on number of common items and common rating interest of users

WANG Xuexia,LI Qing,LI Jihong

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Received:2014-07-16 Revised:2014-07-30 Online:2014-12-01 Published:2014-11-01
Contact: WANG Xuexia

摘要/Abstract

摘要：

在推荐系统中,为了在一定程度上减少用户评分数据稀疏对推荐效果的负面影响,提出了一种基于用户共同评分项目数和用户兴趣的协同过滤推荐算法。此算法将用户共同评分项目数和用户兴趣相似度相结合,使用户之间的相似度计算更加准确,为目标用户提供更好的推荐结果。仿真实验结果表明:所提算法比基于Pearson相似度计算方法的算法推荐效果更优，具有更小的平均绝对误差(MAE),表明了其有效性和可行性。

Abstract:

In order to reduce the negative impacts of sparse data, a new collaborative filtering recommendation algorithm was put forward based on the number of common rating items among users and the similarity of user interests. The similarity calculations were made to be more credible by combing the number of common rating items among users with the similarity of user interests, so as to provide better recommendation results for the target user. Compared with the method based on Pearson similarity, the new algorithm provides better recommendation results with smaller Mean Absolute Error (MAE). In conclusion, the new algorithm is effective and feasible.

中图分类号:

TP301.6

王雪霞李青李季红. 基于主题的Web文本聚类方法[J]. 计算机应用, 2014, 34(11): 3140-3143.

WANG Xuexia LI Qing LI Jihong. Collaborative filtering recommendation based on number of common items and common rating interest of users[J]. Journal of Computer Applications, 2014, 34(11): 3140-3143.

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基于主题的Web文本聚类方法

Collaborative filtering recommendation based on number of common items and common rating interest of users

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