A Time-Sensitive Model for Microblog Retrieval

Microblog, as a way of online communication, can generate large amounts of information in a very short period. Therefore, how to retrieve the latest relevant information becomes a hot research area. Different from traditional information retrieval (IR), the microblog retrieval emphasizes fresh contents of the information. In order to solve this problem, we extend the traditional IR methods by taking into account the posting time. We propose a timesensitive retrieval model, which takes the time factor as a prior probability. In the retrieval model, we introduce the pseudo relevance feedback technology as a query expansion approach to improve retrieval performance. Furthermore, we introduce a strategy to filter the initial retrieval results, which takes post quality factors into account including entropy and link features. Experiments on Twitter corpus show that our algorithm is effective to improve the retrieval performance, and the retrieval results can meet the real time retrieval need well.