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Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more.

In this paper we introduce the query-flow graph, a graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-flow graph a directed edge from query q_i to query q_j means that the two queries are likely to be part of the same "search mission". Any path over the query-flow graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path.

The query-flow graph is an outcome of …