The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Characterizing and predicting search engine switching behavior
Proceedings of the 18th ACM conference on Information and knowledge management
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Predicting query performance on the web
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Predicting query performance using query, result, and user interaction features
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Why searchers switch: understanding and predicting engine switching rationales
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Find it if you can: a game for modeling different types of web search success using interaction data
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Predicting the impact of expansion terms using semantic and user interaction features
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Search sessions are known to be a rich source of diverse valuable information for individual query analysis. In this paper, we address the problem of query performance prediction by utilizing the entire logical search sessions containing the given query. Guided by the intuitions based on the observations made after the analysis of the search sessions' properties and performance of the queries they contain, we propose a number of features that significantly advance the existing query performance prediction models. Some of them specifically allow to focus on tail queries with sparse click-through statistics.