The purpose of the Strategic Workshop in Information Retrieval in Lorne is to explore the
long-range issues of the Information Retrieval field, to recognize challenges that are on-or …

As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …

Machine learning is increasingly applied in high-stakes decision making that directly affect
people's lives, and this leads to an increased demand for systems to explain their decisions …

Learning-to-Rank models based on additive ensembles of regression trees have been
proven to be very effective for scoring query results returned by large-scale Web search …

Search engines are exceptionally important tools for accessing information in today's world.
In satisfying the information needs of millions of users, the effectiveness (the quality of the …

Complex machine learning models are now an integral part of modern, large-scale retrieval
systems. However, collection size growth continues to outpace advances in efficiency …

Learning to Rank (LtR) is the machine learning method of choice for producing high quality
document ranking functions from a ground-truth of training examples. In practice, efficiency …

A variety of applications employ ensemble learning models, using a collection of decision
trees, to quickly and accurately classify an input based on its vector of features. In this paper …

Abstract Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large
amounts of training data to induce high-quality ranking functions. Given a set of documents …

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 …