Practical guide to controlled experiments on the web: listen to your customers not to the hippo
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Weakly-supervised discovery of named entities using web search queries
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A survey of pre-retrieval query performance predictors
Proceedings of the 17th ACM conference on Information and knowledge management
Good abandonment in mobile and PC internet search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Addressing people's information needs directly in a web search result page
Proceedings of the 20th international conference on World wide web
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Abandonment rate is one of the most broadly used online user satisfaction metrics. In this paper we discuss the notion of potential good abandonment, i.e. queries that may potentially result in user satisfaction without the need to click on search results (if search engine result page contains enough details to satisfy the user information need). We show, that we can train a classifier which is able to distinguish between potential good and bad abandonments with rather good results compared to our baseline. As a case study we show how to apply these ideas to IR evaluation and introduce a new metric for A/B-testing -- Bad Abandonment Rate.