...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Autonomous agents and multi-agent systems >Generalized framework for personalized recommendations in agent networks
【24h】

Generalized framework for personalized recommendations in agent networks

机译：代理商网络中个性化推荐的通用框架

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

An agent network can be modeled as a directed weighted graph whose vertices represent agents and edges represent a trust relationship between the agents. This article proposes a new recommendation approach, dubbed LocPat, which can recommend trustworthy agents to a requester in an agent network. We relate the recommendation problem to the graph similarity problem, and define the similarity measurement as a mutually reinforcing relation. We understand an agent as querying an agent network to which it belongs to generate personalized recommendations. We formulate a query into an agent network as a structure graph applied in a personalized manner that reflects the pattern of relationships centered on the requesting agent. We use this pattern as a basis for recommending an agent or object (a vertex in the graph). By calculating the vertex similarity between the agent network and a structure graph, we can produce a recommendation based on similarity scores that reflect both the link structure and the trust values on the edges. Our resulting approach is generic in that it can capture existing network-based approaches merely through the introduction of appropriate structure graphs. We evaluate different structure graphs with respect to two main kinds of settings, namely, social networks and ratings networks. Our experimental results show that our approach provides personalized and flexible recommendations effectively and efficiently based on local information.
机译：代理网络可以建模为有向加权图，其顶点表示代理，边表示代理之间的信任关系。本文提出了一种新的推荐方法，称为LocPat，它可以向代理网络中的请求者推荐可信赖的代理。我们将推荐问题与图相似性问题联系起来，并将相似性度量定义为一个相互增强的关系。我们将代理理解为查询其所属的代理网络以生成个性化推荐。我们将查询表述为代理网络，作为以个性化方式应用的结构图，该结构图反映了以请求代理为中心的关系模式。我们使用此模式作为推荐代理或对象（图中的顶点）的基础。通过计算代理网络和结构图之间的顶点相似度，我们可以基于相似度分数来生成推荐，该相似度分数反映了链接结构和边缘上的信任值。我们产生的方法是通用的，因为它仅通过引入适当的结构图就可以捕获现有的基于网络的方法。我们针对两种主要设置（即社交网络和评分网络）评估不同的结构图。我们的实验结果表明，我们的方法可以根据本地信息有效，高效地提供个性化且灵活的建议。

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