Time-Aware Rank Aggregation for Microblog Search

We tackle the problem of searching microblog posts and frame it as a rank aggregation problem where we merge result lists generated by separate rankers so as to produce a final ranking to be returned to the user. We propose a rank aggregation method, TimeRA, that is able to infer the rank scores of documents via latent factor model-ing. It is time-aware and rewards posts that are published in or near a burst of posts that are ranked highly in many of the lists being ag-gregated. Our experimental results show that it significantly outper-forms state-of-the-art rank aggregation and time-sensitive micro-blog search algorithms