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 updates of the model used for recommending queries provides better recommendations.