摘要
对于规模对等网络,搜索是一个必备的基本功能,但同时是一个困难而又富于挑战性的问题.提出一种基于学习的搜索方法———SmartSearch.SmartSearch采取分布式的被动学习方式,从历史搜索结果中学习节点之间的兴趣相似度,将节点按照兴趣分类,在具有相似兴趣节点之间建立朋友关系.搜索请求首先转发到朋友节点,当其失效后利用广播进行搜索.模拟测试表明,SmartSearch稳定高效,相比传统算法在低开销情况下性能有数量级的提高.
Content search is an essential function, but it presents a very difficult and challenging problem for large-scale peer-to-peer systems. In this paper, a new learning-based algorithm SmartSearch is introduced. SmartSearch learns passively interest similarity between nodes from history search results, divides nodes into interest groups, and constructs friend relations between nodes with similar interest which can be used to locate content effectively. Simulation results show that, compared to the Gnutella algorithm, SmartSearch improves query efficiency by up to ten times without a significant increases in load.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2005年第9期1600-1604,共5页
Journal of Computer Research and Development
基金
国家"九七三"重点基础研究发展规划基金项目(2003CB314802)
国家"八六三"高技术研究发展计划基金项目(2003AA142080)~~
关键词
搜索
对等网络
分类
分布式学习
search
peer-to-peer network
classification
distributed learning
