$$\mathcal {}$$: intelligent recommendation of RSS feeds

The abundance of information prohibits getting relevant results on online social researches. Thus, RSS feeds appear as monitoring tool of current events according to users preferences. However, the user is flooded by the amount of such RSS feeds. For that reason, any analysis of RSS feeds seems effortful and complex. In this paper, we aim to improve the effectiveness and swiftness of pertinent RSS feeds analysis through recommending suitable fragments of queries during the analysis process of events. Accordingly, we propose an innovative architecture of our new active RSS feeds warehouse. Additionally, we introduce a new recommender system to improve the querying expression of RSS feeds. Our experiment results show the robustness and efficiency of our approach.