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 …

Current practice for evaluating recommender systems typically focuses on point estimates of
user-oriented effectiveness metrics or business metrics, sometimes combined with …

In information retrieval, relevance is perceived as a multidimensional and dynamic concept
influenced by user, task, and domain factors. Relying on this perspective, researchers have …

Many real-world decision problems have multiple objectives. For example, when choosing a
medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize …

Purpose The purpose of this paper is to examine strengths and limitations that search
engines (SEs) exhibit when responding to web search queries associated with the grade …

Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from
computing a ranking of final results based on a single metric to multi-objective problems …

Learning to Rank (LTR) technique is ubiquitous in Information Retrieval systems, especially
in search ranking applications. The relevance labels used to train ranking models are often …

We introduce a novel re-ranking model that aims to augment the functionality of standard
search engines to support classroom search activities for children (ages 6 to 11). This model …

To address concerns of TREC-style relevance judgments, we explore two improvements.
The first one seeks to make relevance judgments contextual, collecting in situ feedback of …

Purpose This paper investigates how school teachers look for informational texts for their
classrooms. Access to current, varied and authentic informational texts improves learning …