Information Retrieval

Advanced Topics and Techniques
Deep learning and neural network language models transform search by enhancing indexing, ranking, and query-driven functionality. Covering fundamental architectures, personalized adaptations, and niche verticals like eCommerce, music, and biomedicine, this work presents modern techniques and system design insights.
In the last decade, deep learning and word embeddings have made significant impacts on information retrieval (IR) by adding techniques based in neural networks and language models. At the same time, certain search modalities such as neural IR and conversational search have become more popular. This book, written by international academic and industry experts, brings the field up to date with detailed discussions of these new approaches and techniques. The book is organized in three sections: Foundations, Adaptations and Concerns, and Verticals.

Under Foundations, we address topics that form the basic structure of any modern IR system, including recommender systems. These new techniques are developed to augment indexing, retrieval, and ranking. Neural IR, recommender systems, evaluation, query-driven functionality, and knowledge graphs are covered in this section.

IR systems need to adapt to specific user characteristics and preferences, and techniques that were considered too niche a few years ago are now a matter of system design consideration. The Adaptations and Concerns section covers the following topics: conversational search, cross-language retrieval, temporal extraction and retrieval, bias in retrieval systems, and privacy in search.

While web search engines are the most popular information access point, there are cases where specific verticals provide a better experience in terms of content and relevance. The Verticals section describes eCommerce, professional search, personal collections, music retrieval, and biomedicine as examples.

Contributor: Omar Alonso (Edited by), Ricardo Baeza-Yates (Edited by)

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