This tutorial aims to weave together diverse strands of modern Learning to Rank (LtR) research, and present them in a unified full-day tutorial. First, we will introduce the fundamentals of LtR, and an overview of its various sub-fields. Then, we will ...

Domain-specific Information Retrieval (IR) is generally challenging because of the rare datasets or benchmarks, niche vocabularies and more limited literature coverage. Legal IR is no exception and presents other obstacles, reinforcing the need for ...

We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search context to aid content discovery and support searches for unavailable (...

At Ancestry, we apply learning to rank algorithms to a new area to assist our customers in better understanding their family history. The foundation of our service is an extensive and unique collection of billions of historical records that we have ...

DSSM-like models showed good results in retrieval of short documents that semantically match the query. However, these models require large collections of click-through data that are not available in some domains. On the other hand, the recent advances ...

Giving people the power to build community is central to Facebook's mission. Technically, searching for communities poses very different challenges compared to the standard IR problems. First, there is a vocabulary mismatch problem since most of the ...

Search engines with a loyal user-base face the difficult task of improving overall effectiveness while maintaining the quality of existing work-flows. Risk-sensitive evaluation tools are designed to address that task, but, they currently do not support ...

Visualization of inter-document similarities is widely used for the exploration of document collections and interactive retrieval. However, similarity relationships between documents are multifaceted and measured distances by a given metric often do not ...

Thinking in terms of an information need instead of simply queries provides a rich set of new opportunities in improving the effectiveness of search [6]. User queries may vary a lot for a single information need [3, 9], as a query is often under-...

Events are an integral part of our day-to-day search needs. Users search for various kinds of events such as political events, organizational announcements, policy changes, personal events, criminal activity and so on. In linguistics, events are often ...

Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation. Recently, deep leaning technology has been widely adopted for text matching, making neural text ...

Precision medicine - where data from patients, their genes, their lifestyles and the available treatments and their combination are taken into account for finding a suitable treatment - requires searching the biomedical literature and other resources ...

This tutorial synthesizes and presents research on web tables over the past two decades. We group the tasks into six main categories of information access tasks: (i) table extraction, (ii) table interpretation, (iii) table search, (iv) question ...

Information retrieval (IR) models often employ complex variations in term weights to compute an aggregated similarity score of a query-document pair. Treating IR models as black-boxes makes it difficult to understand or explain why certain documents are ...

Recommender systems are widely used in many applications, e.g., social network, e-commerce. Inner product retrieval IPR is the core subroutine in Matrix Factorization (MF) based recommender systems. It consists of two phases: i) inner product ...

We observe that most relevant terms in unstructured news articles are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 ...

Word embeddings have gained significant attention as learnable representations of semantic relations between words, and have been shown to improve upon the results of traditional word representations. However, little effort has been devoted to using ...

When shopping for fashion, customers often look for products which can complement their current outfit. For example, customers want to buy a jacket which can go well with their jeans and sneakers. To address the task of fashion matching, we propose a ...

Learning-to-Rank is a branch of supervised machine learning that seeks to produce an ordering of a list of items such that the utility of the ranked list is maximized. Unlike most machine learning techniques, however, the objective cannot be directly ...

Blending of search results from several vertical sources became standard among web search engines. Similar scenarios appear in computational advertising, news recommendation, and other interactive systems. As such environments give only partial feedback,...