基于用户行为的长查询用户满意度分析

Long Query User Satisfaction Analysis Based on User Behaviors

搜索引擎性能评估是信息检索界一个重要课题.长查询具有较为丰富的信息内容,能更加准确地描述用户的信息需求.在此基础上文中提出长查询用户满意度分析的整体框架,定义用户满意度的概念,并在用户日志中提取相关用户行为特征,应用决策树和SVM两种分类算法评测用户满意度.在大规模商业搜索引擎日志上完成的实验结果证明了这套评价体系的有效性.结果表明,用户对于查询满意和不满意的分类准确率分别达到86%和70%.

Performance evaluation is one of the most important issues in web search. Long queries contain much information which describes user＇s information demand correctly. Thus, a long query search user satisfaction detection framework is proposed. The concept of user satisfaction is defined. The relevant user behavior features in user logs are extracted which are combined with Decision Tree and SVM to identify satisfactory or unsatisfactory queries. The experimental results on large scale practical search engine data show the effectiveness of the proposed framework. Furthermore, the classification accuracies of satisfactory and unsatisfactory queries reach 86% and 70% , respectively.