A survey on session detection methods in query logs and a proposal for future evaluation

Abstract

Search engine logs provide a highly detailed insight of users’ interactions. Hence, they are both extremely useful and sensitive. The datasets publicly available to scholars are, unfortunately, too few, too dated and too small. There are few because search engine companies are reluctant to release such data; they are dated because they were collected in late 1990s or early 2000s; and they are small because they comprise data for at most one day and just a few hundreds of thousands of users. Even worse, the large query log disclosed by AOL in 2006 caused more harm than good because of a big privacy flaw. In this paper the author provides an overall view of the possible applications of query logs, the privacy concerns researchers must face when working on such datasets, and several ways in which query logs can be easily sanitized. One of such measures consists of segmenting the logs into short topical sessions. Therefore, the author offers a comprehensive survey of session detection methods, as well as a thorough description of a new evaluation framework with performance results for each of the different methods. Additionally, a new, simple, but outperforming session detection method is proposed. It is a heuristic-based technique which works on the basis of a geometric interpretation of both the time gap between queries and the similarity between them in order to flag a topic shift