A modular system of algorithms for unconstrained minimization
ACM Transactions on Mathematical Software (TOMS)
How people revisit web pages: empirical findings and implications for the design of history systems
International Journal of Human-Computer Studies - Special issue: World Wide Web usability
ACM SIGIR Forum
Stuff I've seen: a system for personal information retrieval and re-use
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Information search and re-access strategies of experienced web users
WWW '05 Proceedings of the 14th international conference on World Wide Web
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
History repeats itself: repeat queries in Yahoo's logs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A large-scale analysis of query logs for assessing personalization opportunities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Information re-retrieval: repeat queries in Yahoo's logs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Personalized Delivery of On---Line Search Advertisement Based on User Interests
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Mining rich session context to improve web search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Using word-sense disambiguation methods to classify web queries by intent
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Do you want to take notes?: identifying research missions in Yahoo! search pad
Proceedings of the 19th international conference on World wide web
On the quality of inferring interests from social neighbors
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Graph structures and algorithms for query-log analysis
CiE'10 Proceedings of the Programs, proofs, process and 6th international conference on Computability in Europe
Action prediction and identification from mining temporal user behaviors
Proceedings of the fourth ACM international conference on Web search and data mining
Efficient diversification of web search results
Proceedings of the VLDB Endowment
Modeling and analysis of cross-session search tasks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Extending Open Directory Project to represent user interests
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Similarity caching in large-scale image retrieval
Information Processing and Management: an International Journal
The impact of images on user clicks in product search
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Modeling the impact of short- and long-term behavior on search personalization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Studies of the onset and persistence of medical concerns in search logs
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Search, interrupted: understanding and predicting search task continuation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Role-explicit query identification and intent role annotation
Proceedings of the 21st ACM international conference on Information and knowledge management
An Investigation of User Behaviour Consistency for Context-Aware Information Retrieval Systems
International Journal of Advanced Pervasive and Ubiquitous Computing
Personalization of web-search using short-term browsing context
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Web search engines consistently collect information about users interaction with the system: they record the query they issued, the URL of presented and selected documents along with their ranking. This information is very valuable: It is a poll over millions of users on the most various topics and it has been used in many ways to mine users interests and preferences. Query logs have the potential to partially alleviate the search engines from thousand of searches by providing a way to predict answers for a subset of queries and users without knowing the content of a document. Even if the predicted result is at rank one, this analysis might be of interest: If there is enough confidence on a user's click, we might redirect the user directly to the page whose link would be clicked. In this paper, we present three different models for predicting user clicks, ranging from most specific ones (using only past user history for the query) to very general ones (aggregating data over all users for a given query). The former model has a very high precision at low recall values, while the latter can achieve high recalls. We show that it is possible to combine the different models to predict with high accuracy (over 90%) a high subset of query sessions (24% of all the sessions).