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 …

The importance of tasks in information retrieval (IR) has been long argued for, addressed in
different ways, often ignored, and frequently revisited. For decades, scholars made a case …

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 …

We present a user modeling system that serves as the foundation of a personal assistant.
The system ingests web search history for signed-in users, and identifies coherent contexts …

Users' search tasks have become increasingly complicated, requiring multiple queries and
interactions with the results. Recent studies have demonstrated that modeling the historical …

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 …

Recent studies show that historical behaviors (such as queries and their clicks) contained in
a search session can benefit the ranking performance of subsequent queries in the session …

Search engines must accurately predict the implicit intent of users to effectively guide their
online search experience and assist them in completing their tasks. Users create time …

We consider a search task as a set of queries that serve the same user information need.
Analyzing search tasks from user query streams plays an important role in building a set of …

A significant amount of search queries originate from some real world information need or
tasks [13]. In order to improve the search experience of the end users, it is important to have …