Query session detection as a cascade

We propose a cascading method for query session detection, the problem of identifying series of consecutive queries a user submits with the same information need. While the existing session detection research mostly deals with effectiveness, our focus also is on efficiency, and we investigate questions related to the analysis trade-off: How expensive (in terms of runtime) is a certain improvement in F-Measure? In this regard, we distinguish two major scenarios where query session knowledge is important: (1) In an online setting, the search engine tries to incorporate knowledge of the preceding queries for an improved retrieval performance. Obviously, the efficiency of the session detection method is a crucial issue as the overall retrieval time should not be influenced too much. (2) In an offline post-retrieval setting, search engine logs are divided into sessions in order to examine what causes users to fail or to identify typical reformulation patterns etc. Here, efficiency might not be as important as in the online scenario but the accuracy of the detected sessions is essential. Our cascading method provides a sensible treatment for both scenarios. It involves different steps that form a cascade in the sense that computationally costly and hence time-consuming features are applied only after cheap features "failed." This is different to previous session detection methods, most of which involve many features simultaneously. Experiments on a standard test corpus show the cascading method to save runtime compared to the state of the art while the detected sessions' accuracy is even superior.