IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
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
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Matching task profiles and user needs in personalized web search
Proceedings of the 17th ACM conference on Information and knowledge management
Friends, romans, countrymen: lend me your URLs. using social chatter to personalize web search
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Personalized search in digital libraries via spreading activation model
Web Intelligence and Agent Systems
Automatic task-based profile representation for content-based recommendation
International Journal of Knowledge-based and Intelligent Engineering Systems
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Search personalization has been pursued in many ways, in order to provide better result rankings and better overall search experience to individual users [5]. However, blindly applying personalization to all user queries, for example, by a background model derived from the user's long-term query-and-click history, is not always appropriate for aiding the user in accomplishing her actual task. User interests change over time, a user sometimes works on very different categories of tasks within a short timespan, and history-based personalization may impede a user's desire of discovering new topics. In this paper we propose a personalization framework that is selective in a twofold sense. First, it selectively employs personalization techniques for queries that are expected to benefit from prior history information, while refraining from undue actions otherwise. Second, we introduce the notion of tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics, and base our reasoning selectively on query-relevant user tasks. These considerations are cast into a statistical language model for tasks, queries, and documents, supporting both judicious query expansion and result re-ranking. The effectiveness of our method is demonstrated by an empirical user study.