【摘要】 针对现有个性化推荐服务系统中用户会话聚类算法存在相似性度量准确性低和需要事先确定聚类数目的问题,对序化的用户访问页面和对应的访问时间信息进行整合,提出一种基于动态规划算法的全序列比对方法来度量用户会话的相似性。在此基础上,运用改进的NJW谱聚类算法对用户会话进行自动谱聚类。实验结果表明,算法充分考虑了用户会话的整体特征和局部信息,较相关比对算法具有更高的聚类性能,可以提高网站个性化推荐服务的效率。更多还原

【Abstract】 Focusing on the problem of low accuracy of similarity measurement and necessarily determining the number of clustering in advance in clustering algorithms of user sessions in existing personalized recommendation services systems,a global alignment method based on dynamic programming algorithm was proposed to measure similarity between user sessions by integrating the information of serialized visiting pages and visiting times.On this basis,automatic spectral clustering was done on the user sessions by using improved NJW clustering algorithm.Experimental results show that the algorithm achieves a higher clustering performance than the comparative algorithms by considering the overall characteristics and local information of user sessions.It can also improve the efficiency of Web personalized recommendation services.更多还原