AutoEval: an evaluation methodology for evaluating query suggestions using query logs
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Applying web usage mining for adaptive intranet navigation
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
Moving towards adaptive search in digital libraries
NLP4DL'09/AT4DL'09 Proceedings of the 2009 international conference on Advanced language technologies for digital libraries
Exploring ant colony optimisation for adaptive interactive search
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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While much research has been performed on query logs collected for major Web search engines, query log analysis to enhance search on smaller and more focused collections has attracted less attention. Our hypothesis is that an intranet search engine can be enhanced by adapting the search system to real users' search behaviour through exploiting its query logs. In this work we describe how a constantly adapting domain model can be used to identify and capture changes in intranet users' search requirements over time. We employ an algorithm that dynamically builds a domain model from query modifications taken from an intranet query log and employs a decay measure, as used in Machine Learning and Optimisation methods, to promote more recent terms. This model is used to suggest query refinements and additions to users and to elevate seasonally relevant terms. A user evaluation using models constructed from a substantial university intranet query log is provided. Statistical evidence demonstrates the system's ability to suggest seasonally relevant terms over three different academic trimesters. We conclude that log files of an intranet search engine are a rich resource to build adaptive domain models, and in our experiments these models significantly outperform sensible baselines.