摘要
搜索引擎返回的结果太多且不能根据用户的兴趣提供检索结果是当前较受关注的问题。把用户兴趣模型和STC聚类算法相结合,提出了改进的STC算法,并提出个性化推荐的策略和兴趣描述更新的方法,实现了一个基于搜索结果的个性化推荐系统(SRPRS)。SRPRS基于改进的STC算法自动组织搜索结果,帮助用户利用主题的方式发现所需的资源。通过实验,分析了SRPRS系统的聚类特性和时间特性。针对搜索引擎的列表显示结果,SRPRS系统在快速查找用户感兴趣的文档上有较好的性能。
It is a remarkable problem that the amount of results returned by search engines is too large and the engines can＇t provide the users with required results based on their interest.This paper puts forward an improved STC algorithm by combining user-interest model with STC clustering algorithm.In addition,a policy of personalization recommendation and a method of updating user-interest profile are also proposed at the same time.A personalization recom mendation system based on search resuh（SRPRS） has been implemented by the use of the improved STC algorithm, SRPRS can organize search results automatically,and help the users find the needed results with the specific topics.Finally,the paper analyzes the clustering and time properties of the SRPRS by experiments,In allusion to the search result list,SRPRS has a better performance in fast finding the user＇s interested documents.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第32期150-153,共4页
Computer Engineering and Applications
基金
河南省重大科技攻关项目(0222020600)
河南省优秀中青年骨干教师资助项目(2002-2005)。
关键词
搜索结果
聚类
兴趣描述
个性化推荐
search result
clustering
interest profile
personalization recommendation
