Books in the Foundations and Trends (R) in Information Retrieval series

Provides a complete picture of neural information retrieval techniques that culminate in supervised neural learning to rank models including deep neural network architectures that are trained end-to-end for ranking tasks. In reaching this point, the authors cover all the important topics.

The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching patterns from data. This survey gives a systematic and comprehensive introduction to the deep matching models for search and recommendation.

Focuses predominantly on the problems and solutions proposed in traditional areas while also looking briefly at the emerging areas. To facilitate future research, a discussion of available resources, a list of public benchmark datasets and a discussion on future research directions are provided in the concluding sections.

Offers a survey of the science and practice of web crawling. This survey outlines the fundamental challenges and describes state-of-the-art models and solutions. It also highlights avenues for future work.

Comprehensively reviews the foundations of search engines, from index layouts to basic query processing strategies, while also providing the latest trends in the literature in efficient query processing. It goes on to describe techniques in applying a cascading infrastructure within search systems.

Provides a comprehensive review of explainable recommendation research. The authors first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W. They then conduct a comprehensive survey of explainable recommendation.

Offers the first survey of neural approaches to conversational AI that targets Natural Language Processing and Information Retrieval audiences. The book provides a csurvey of the neural approaches to conversational AI that have been developed, covering QA, task-oriented and social bots with a unified view of optimal decision making.

Provides an overview of bandit algorithms inspired by various aspects of Information Retrieval (IR), such as click models, online ranker evaluation, personalization or the cold-start problem. Using a survey style, each chapter focuses on a specific IR problem and explains how it was addressed with various bandit approaches.

In this concise history of the early years of information retrieval, Donna Harman, one of the pioneers of the field, provides the reader with a plethora of insights into the important work that led us to where we are today. Written in a chronological order, this book lays out how each contribution built on what went before.