Comparing Client and Server Dwell Time Estimates for Click-Level Satisfaction Prediction

被引:18
作者
Kim, Youngho [1 ,3 ]
Hassan, Ahmed [2 ]
White, Ryen W. [2 ]
Zitouni, Imed [2 ]
机构
[1] Univ Massachusetts, 140 Governors Dr, Amherst, MA 01003 USA
[2] Microsoft, Redmond, WA 98052 USA
[3] Microsoft Res, Redmond, WA USA
来源
SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2014年
关键词
Dwell time analysis; Click satisfaction;
D O I
10.1145/2600428.2609468
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Click dwell time is the amount of time that a user spends on a clicked search result. Many previous studies have shown that click dwell time is strongly correlated with result-level satisfaction and document relevance. Accurate estimates of dwell time are therefore important for applications such as search satisfaction prediction and result ranking. However, dwell time can be estimated in different ways according to the information available about the search process. For example, a result reached for the query [Garfield] may involve 145s of "server-side" dwell time (observable to the search engine) and 40s of "client-side" dwell time (observable from the browser). Since search engines can only observe server-side actions (i.e., activity on the search engine result page), server-side dwell times are estimated by measuring the time between a search result click and the next search event (click or query). Conversely, more detailed information about page dwell times can be obtained via client-side methods such as Web browser toolbars. The client-side information enables the estimation of more accurate dwell times by measuring the amount of time that a user spends on pages of interest (either the landing page, or pages on the full navigation trail). In this paper, we define three different dwell times, i.e., server-side, client-side, and trail dwell time, and examine their effectiveness for predicting click satisfaction. For this, we collect toolbar and search engine logs from real users, and provide an analysis of dwell times for improving prediction performance. Moreover, we show further improvements in predicting click-level satisfaction by combining dwell times with other query features (e.g., query clarity).
引用
收藏
页码:895 / 898
页数:4
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