Measuring and Predicting Search Engine Users' Satisfaction

被引:24
作者
Dan, Ovidiu [1 ]
Davison, Brian D. [1 ]
机构
[1] Lehigh Univ, Comp Sci & Engn Dept, Packard Lab, 19 Mem Dr West, Bethlehem, PA 18015 USA
关键词
Measurement; Experimentation; Algorithms; Human Factors; Abandonment; advanced users; browser plugin logs; browser toolbar logs; novice users; predicting satisfaction; query difficulty; query logs; query performance; satisfaction; search engine evaluation; search engine switching prediction; search sessions; search success; search tasks; task completion; task difficulty; user behavior models; user dissatisfaction; user frustration; user satisfaction; web search success;
D O I
10.1145/2893486
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Search satisfaction is defined as the fulfillment of a user's information need. Characterizing and predicting the satisfaction of search engine users is vital for improving ranking models, increasing user retention rates, and growing market share. This article provides an overview of the research areas related to user satisfaction. First, we show that whenever users choose to defect from one search engine to another they do so mostly due to dissatisfaction with the search results. We also describe several search engine switching prediction methods, which could help search engines retain more users. Second, we discuss research on the difference between good and bad abandonment, which shows that in approximately 30% of all abandoned searches the users are in fact satisfied with the results. Third, we catalog techniques to determine queries and groups of queries that are underperforming in terms of user satisfaction. This can help improve search engines by developing specialized rankers for these query patterns. Fourth, we detail how task difficulty affects user behavior and how task difficulty can be predicted. Fifth, we characterize satisfaction and we compare major satisfaction prediction algorithms.
引用
收藏
页数:35
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