This paper provides a unified account of two schools of thinking in information retrieval
modelling: the generative retrieval focusing on predicting relevant documents given a query …

This paper studies a new scenario in conversational search, conversational question
suggestion, which leads search engine users to more engaging experiences by suggesting …

Search, recommendation, and online advertising are the three most important information-
providing mechanisms on the web. These information seeking techniques, satisfying users' …

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 …

One of the central issues in learning to rank for information retrieval is to develop algorithms
that construct ranking models by directly optimizing evaluation measures such as …

Exploratory search requires the system to assist the user in comprehending the information
space and expressing evolving search intents for iterative exploration and retrieval of …

In this paper we address the issue of learning diverse ranking models for search result
diversification. Typical methods treat the problem of constructing a diverse ranking as a …

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 …

This paper concerns reinforcement learning~(RL) of the document ranking models for
information retrieval~(IR). One branch of the RL approaches to ranking formalize the …