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Measuring and Predicting Search Engine Users' Satisfaction
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DOI：10.1145/2893486.png)
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Abstract
En 中文
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.
Keywords:
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
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