This paper studies conversational approaches to information retrieval, presenting a theory
and model of information interaction in a chat setting. In particular, we consider the question …

A well-known challenge in learning from click data is its inherent bias and most notably
position bias. Traditional click models aim to extract the‹ query, document› relevance and …

Click-through data has proven to be a critical resource for improving search ranking quality.
Though a large amount of click data can be easily collected by search engines, various …

Modern search engines leverage a variety of sources, beyond the conventional query-
document content similarity, to improve their ranking performance. Among them, query …

Modern email clients support predicate-based folder assignment rules that can automatically
organize emails. Unfortunately, users still need to write these rules manually. Prior machine …

User interaction data (eg, click data) has proven to be a powerful signal for learning-to-rank
models in web search. However, such models require observing multiple interactions across …

As the number of email users and messages continues to grow, search is becoming more
important for finding information in personal archives. In spite of its importance, email search …

In the enterprise email search setting, the same search engine often powers multiple
enterprises from various industries: technology, education, manufacturing, etc. However …

Synonym expansion is a technique that adds related words to search queries, which may
lead to more relevant documents being retrieved, thus improving recall. There is extensive …

This work studies the effectiveness of query expansion for email search. Three state-of-the-
art expansion methods are examined: 1) a global translation-based expansion model; 2) a …