Evaluating implicit measures to improve web search
Evaluating implicit measures to improve web search
复制标题
DOI：
10.1145/1059981.1059982
复制
发表时间：
2005-04-01
影响因子：
5.6
通讯作者：
White, T
中科院分区：
文献类型：
--
作者：
Fox, S;Karnawat, K;White, T
在改善搜索体验的领域中，越来越多的兴趣是收集隐式用户行为度量（隐式度量）作为用户兴趣和用户满意度的指示。而不是必须提交明确的用户反馈，这可能是昂贵的时间和资源，并改变使用模式内的搜索体验，一些研究探索了收集的隐式措施作为一个有效的和有用的替代收集明确的措施，从users.This研究文章介绍了最近的一项研究，有两个主要目标。第一个是测试用户满意度的明确评级和用户兴趣的隐含度量之间是否存在关联。第二个是了解哪些隐性措施与用户满意度最密切相关。感兴趣的领域是网络搜索。我们开发了一个仪表化的浏览器来收集用户活动的各种措施，并要求明确的判断访问的各个页面和整个搜索会话的相关性。在工作场所收集数据，以提高结果的普遍性。使用传统方法（例如，贝叶斯建模和决策树）以及新的使用行为模式分析（“基因分析”）。我们发现，有一个用户活动和用户的明确的满意度评级之间的隐式措施的关联。单个页面的最佳模型结合了点击量、在搜索结果页面上花费的时间以及用户如何退出结果或结束搜索会话（退出类型/结束操作）。行为模式（通过基因分析）也可以用来预测用户对搜索会话的满意度。
Of growing interest in the area of improving the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedback, which can be costly in time and resources and alter the pattern of use within the search experience, some research has explored the collection of implicit measures as an efficient and useful alternative to collecting explicit measure of interest from users.This research article describes a recent study with two main objectives. The first was to test whether there is an association between explicit ratings of user satisfaction and implicit measures of user interest. The second was to understand what implicit measures were most strongly associated with user satisfaction. The domain of interest was Web search. We developed an instrumented browser to collect a variety of measures of user activity and also to ask for explicit judgments of the relevance of individual pages visited and entire search sessions. The data was collected in a workplace setting to improve the generalizability of the results.Results were analyzed using traditional methods (e.g., Bayesian modeling and decision trees) as well as a new usage behavior pattern analysis ("gene analysis"). We found that there was an association between implicit measures of user activity and the user's explicit satisfaction ratings. The best models for individual pages combined clickthrough, time spent on the search result page, and how a user exited a result or ended a search session (exit type/end action). Behavioral patterns (through the gene analysis) can also be used to predict user satisfaction for search sessions.