Computational Statistics & Data Analysis - Nonlinear methods and data mining
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Predictive user click models based on click-through history
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Predicting when browsing context is relevant to search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
Proceedings of the 17th ACM conference on Information and knowledge management
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Assessing the scenic route: measuring the value of search trails in web logs
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Personalizing web search using long term browsing history
Proceedings of the fourth ACM international conference on Web search and data mining
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
Personalizing web search results by reading level
Proceedings of the 20th ACM international conference on Information and knowledge management
Context-aware search personalization with concept preference
Proceedings of the 20th ACM international conference on Information and knowledge management
Probabilistic models for personalizing web search
Proceedings of the fifth ACM international conference on Web search and data mining
Trustworthy online controlled experiments: five puzzling outcomes explained
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and 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
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Search and browsing activity is known to be a valuable source of information about user's search intent. It is extensively utilized by most of modern search engines to improve ranking by constructing certain ranking features as well as by personalizing search. Personalization aims at two major goals: extraction of stable preferences of a user and specification and disambiguation of the current query. The common way to approach these problems is to extract information from user's search and browsing long-term history and to utilize short-term history to determine the context of a given query. Personalization of the web search for the first queries in new search sessions of new users is more difficult due to the lack of both long- and short-term data. In this paper we study the problem of short-term personalization. To be more precise, we restrict our attention to the set of initial queries of search sessions. These, with the lack of contextual information, are known to be the most challenging for short-term personalization and are not covered by previous studies on the subject. To approach this problem in the absence of the search context, we employ short-term browsing context. We apply a widespread framework for personalization of search results based on the re-ranking approach and evaluate our methods on the large scale data. The proposed methods are shown to significantly improve non-personalized ranking of one of the major commercial search engines. To the best of our knowledge this is the first study addressing the problem of short-term personalization based on recent browsing history. We find that performance of this re-ranking approach can be reasonably predicted given a query. When we restrict the use of our method to the queries with largest expected gain, the resulting benefit of personalization increases significantly