Refreshing models to provide timely query recommendations

In this work we propose a comparative study of the effects of a continuous model update on the effectiveness of well- known query recommendation algorithms. In their original formulation, these algorithms use static (i.e. pre-computed) models to generate recommendations. We extend these algorithms to generate suggestions using: a static model (no updates), a model updated periodically, and a model continuously updating (i.e. each time a query is submitted). We assess the results by previously proposed evaluation metrics and we show that the use of periodical and continuous up- dates of the model used for recommending queries provides better recommendations. Copyright owned by the authors.