一种基于聚类技术的个性化信息检索方法

实践证明聚类技术是改进搜索结果显示方式的一种有效手段。然而,目前的聚类方法没有考虑到用户兴趣,对于相同的查询,返回给所有用户同样的聚类结果。由此提出一种个性化聚类检索方法。该方法改进了k-means算法,利用该算法对传统搜索引擎返回的结果结合用户兴趣进行聚类,返回针对特定用户的网页簇。实验证明该方法能够提供个性化服务,改善了聚类的效果,提高了用户的检索效率。

Personalized search based on clustering

Clustering display of search results has been proved an efficient way to organize the Web resources.However,for a given query,clustering results reached by any user are totally identical.A novel search method based on clustering is proposed,which is a modified version of k-means algorithm.The results generated from usual search engine go through a clustering stage based on user interests to create user-specific clusters.Experiments show that it can offer user-specific information needs,improve clustering effectiveness and searching efficiency.