• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>User modeling, adaptation, and personalization >Bayesian Credibility Modeling for Personalized Recommendation in Participatory Media
【24h】

Bayesian Credibility Modeling for Personalized Recommendation in Participatory Media

机译：参与媒体中个性化推荐的贝叶斯可信度建模

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

In this paper, we focus on the challenge that users face in processing messages on the web posted in participatory media settings, such as blogs. It is desirable to recommend to users a restricted set of messages that may be most valuable to them. Credibility of a message is an important criteria to judge its value. In our approach, theories developed in sociology, political science and information science are used to design a model for evaluating the credibility of messages that is user-specific and that is sensitive to the social network in which the user resides. To recommend new messages to users, we employ Bayesian learning, built on past user behaviour, integrating new concepts of context and completeness of messages inspired from the strength of weak ties hypothesis, from social network theory. We are able to demonstrate that our method is effective in providing the most credible messages to users and significantly enhances the performance of collaborative filtering recommendation, through a user study on the digg.com dataset.
机译：在本文中，我们着眼于用户在处理参与性媒体设置（例如博客）中发布的Web消息时面临的挑战。希望向用户推荐可能对他们最有价值的一组受限制的消息。消息的可信度是判断其价值的重要标准。在我们的方法中，使用社会学，政治学和信息科学中发展的理论来设计一种模型，以评估特定于用户且对用户所居住的社交网络敏感的消息的可信度。为了向用户推荐新消息，我们采用基于过去用户行为的贝叶斯学习方法，结合了弱关系假设的力量和社交网络理论的启发，结合了上下文和消息完整性的新概念。通过对digg.com数据集的用户研究，我们能够证明我们的方法可有效地向用户提供最可信的消息，并显着增强了协同过滤推荐的性能。

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