Logging is a common practice in software engineering to provide insights into working
systems. The main uses of log files have always been failure identification and root cause …

In information retrieval, query auto completion (QAC), also known as typeahead [Xiao et al.,
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …

Large language models (LLMs) have shown impressive capabilities in natural language
understanding and generation. Their potential for deeper user understanding and improved …

Personalized search systems tailor search results to the current user intent using historic
search interactions. This relies on being able to find pertinent information in that user's …

The amounts of available Semantic Web (SW) data (including Linked Open Data) constantly
increases. Users would like to browse and explore effectively such information spaces …

To understand whether a user is satisfied with the current search results, implicit behavior is
a useful data source, with clicks being the best-known implicit signal. However, it is possible …

In a conversational context, a user converses with a system through a sequence of natural-
language questions, ie, utterances. Starting from a given subject, the conversation evolves …

Search tasks, comprising a series of search queries serving the same information need,
have recently been recognized as an accurate atomic unit for modeling user search intent …

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 …

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 …