We present a context-aware neural ranking model to exploit users' on-task search activities
and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural …

Context information in search sessions has proven to be useful for capturing user search
intent. Existing studies explored user behavior sequences in sessions in different ways to …

Session search is a complex search task that involves multiple search iterations triggered by
query reformulations. We observe a Markov chain in session search: user's judgment of …

Search logs contain examples of frequently occurring patterns of user reformulations of
queries. Intuitively, the reformulation" San Francisco"--" San Francisco 49ers" is semantically …

To better exploit the search logs, various click models have been proposed to extract implicit
relevance feedback from user clicks. Most traditional click models are based on probability …

Users' complex information needs usually require consecutive queries, which results in
sessions with a series of interactions. Exploiting such contextual interactions has been …

Context such as the user's search history, demographics, devices, and surroundings, has
become prevalent in various domains of information seeking and retrieval such as mobile …

Information Retrieval (IR) research has traditionally focused on serving the best results for a
single query-so-called ad hoc retrieval. However, users typically search iteratively, refining …

Dialogue management (DM) is responsible for predicting the next action of a dialogue
system according to the current dialogue state and thus plays a central role in task-oriented …

To support complex search tasks, where the initial information requirements are complex or
may change during the search, a search engine must adapt the information delivery as the …