Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Unsupervised query segmentation using generative language models and wikipedia
Proceedings of the 17th international conference on World Wide Web
Pfp: parallel fp-growth for query recommendation
Proceedings of the 2008 ACM conference on Recommender systems
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
PSkip: estimating relevance ranking quality from web search clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward self-correcting search engines: using underperforming queries to improve search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Search engines record a large amount of metadata each time a user issues a query. While efficiently mining this data can be challeng-ing, the results can be useful in multiple ways, including monitoring search engine performance, improving search relevance, prioritizing research, and optimizing day-to-day operations. In this poster, we describe an approach for mining query log data for actionable insights - specific query segments (sets of queries) that require attention, and actions that need to be taken to improve the segments. Starting with a set of important metrics, we identify query segments that are "interesting" with respect to these metrics using a distributed frequent itemset mining algorithm.