• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Chinese Journal of Electronics >Improve the Collaborative Filtering Recommender System Performance by Trust Network Construction
【24h】

Improve the Collaborative Filtering Recommender System Performance by Trust Network Construction

机译：通过信任网络构建提高协同过滤推荐系统的性能

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Data sparseness brings significant challenges to the research of recommender systems. It becomes more severe for neighborhood-based collaborative filtering. We introduce the trust relation computing of the sociology field. Instead of the traditional similarity computing method, the trust degree is integrated for the nearest neighbor selection. The trust network is constructed by the expansion of different path length, and the trust value between the users can be obtained by the trust transmission rules. To verify the effectiveness of our method, we give the experiments on different techniques for rating prediction, including Pearson based method, the User position similarity (UPS) based method and the trust with Pearson and UPS. We also give the t-test result. The implementation of the experiment on the Epinions data set shows that the proposed method can improve the system performance significantly.
机译：数据稀疏性对推荐系统的研究提出了重大挑战。对于基于邻域的协作过滤，它变得更加严重。我们介绍了社会学领域的信任关系计算。代替传统的相似度计算方法，将信任度集成到最近邻居选择中。信任网络是通过扩展不同的路径长度来构造的，用户之间的信任值可以通过信任传输规则来获得。为了验证我们方法的有效性，我们针对不同的评分预测技术进行了实验，包括基于Pearson的方法，基于用户位置相似度（UPS）的方法以及与Pearson和UPS的信任度。我们还给出了t检验的结果。在Epinions数据集上进行的实验表明，该方法可以显着提高系统性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号