...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of web services research >Over-Fitting and Error Detection for Online Role Mining
【24h】

Over-Fitting and Error Detection for Online Role Mining

机译：在线角色挖掘的过度拟合和错误检测

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

Recent research has attempted to use role-based approaches to recommend mobile services to other members among the same group in a context dependent manner. However, the traditional role mining approaches originated from the domain of security control tend to be rigid and may not be able to capture human behaviors adequately. In particular, during the course of role mining process, these approaches easily result in over-fitting, i.e., too many roles with slightly different service consumption patterns are found. As a result, they fail to reveal the true common preferences within the user community. This paper proposes an online role mining algorithm with a residual term and an error term, that automatically group users according to their interests and habits without losing sight of their individual preferences and random errors. Moreover, to resolve the over-fitting problem, the authors relax the role definition in role mining mechanism by introducing quasi-roles based on the concept of quasi-bicliques. Most importantly, the new concept allows us to propose a monitoring framework to detect and correct over-fitting in online role mining such that recommendations can be made based on the latest and genuine common preferences. To the best of the authors 'knowledge, this is a new area in service recommendation that is yet to be fully explored.
机译：最近的研究已尝试使用基于角色的方法以上下文相关的方式向同一组中的其他成员推荐移动服务。但是，源自安全控制领域的传统角色挖掘方法往往很僵化，可能无法充分捕捉人类行为。特别地，在角色挖掘过程中，这些方法很容易导致过度拟合，即，发现太多具有稍微不同的服务消耗模式的角色。结果，他们无法揭示用户社区内真正的共同偏好。本文提出了一种带有残差项和错误项的在线角色挖掘算法，该算法可以根据用户的兴趣和习惯将其自动分组，而不会忽略他们的个人偏好和随机错误。此外，为了解决过度拟合的问题，作者通过引入基于拟比对的概念的拟角色来放松角色挖掘机制中的角色定义。最重要的是，新概念使我们能够提出一个监视框架，以检测和纠正在线角色挖掘中的过度适合问题，从而可以根据最新和真正的共同偏好提出建议。就作者所知，这是服务推荐中的一个新领域，尚待充分探索。

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