Bøger i Foundations and Trends (R) in Information Retrieval serien

Reviews research on the design and evaluation of search user interfaces of the past 10 years. It integrates state-of-the-art research in the areas of information seeking behavior, information retrieval, and human-computer interaction on the topic of search interface.

Covers five areas related to the mining of user interests from social media: the foundations of social user interest modeling; techniques that have been adopted; evaluation methodologies and benchmark datasets; applications that have been taking advantage of user interest mining; and challenges, research questions, and opportunities.

Surveys two important components of modern information access: information retrieval (IR) and knowledge graphs (KGs). The authors provide an overview of the literature on KGs in the context of IR and the components required when building IR systems that leverage KGs.

Presents a thorough review of the state of the art of recommender systems that leverage psychological constructs and theories to model and predict user behaviour and improve the recommendation process - so-called psychology-informed recommender systems.

Provides both historical background and a comprehensive overview of the recent advances in the field. In particular, the authors lay out all the components needed in the design of a privacy-centric email search engine.

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.

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.

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.

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.

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.

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 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.

Provides a comprehensive summary of previous research in aggregated search. The book starts by describing why aggregated search requires unique solutions. It concludes by highlighting the main trends and discussing short-term and long-term areas for future work.

Provides a comprehensive overview of the broad area of semantic search on text and knowledge bases. Semantic search is studied in a variety of different communities with a variety of different views of the problem.

Online evaluation is one of the most common approaches to measure the effectiveness of an information retrieval system. It involves fielding the information retrieval system to real users, and observing these users' interactions in situ while they engage with the system. This book provides the reader with a comprehensive overview of the topic.

Provides researchers who are working on query auto completion or related problems in the field of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. This book also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions.

Provides a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and genre specific summarization and for evaluation of summarization.

Presents a detailed analysis of existing credibility models from different information seeking research areas, with a focus on the Web and its pervasive social component. This book shows that there is a very rich body of work pertaining to different aspects and interpretations of credibility, particularly for different types of textual content.

Over the past decade, the information retrieval community has explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. This book provides a coherent and organised survey on this topic.

Provides a comprehensive overview of temporal information retrieval approaches, centred on the following questions: what are temporal dynamics, why do they occur, and when and how to leverage temporal information throughout the search cycle and architecture?

Surveys the young but established field of research that is Music Information Retrieval (MIR). In doing so, this book pays particular attention to the latest developments in MIR, such as semantic auto-tagging and user-centric retrieval and recommendation approaches.

Provides a systematic and detailed introduction to newly developed machine learning technologies for query document matching in search, particularly in web search. The book focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form, phrase, word sense, topic, and structure aspect.

Reviews the published literature on search result diversification. In particular, the book discusses the motivations for diversifying the search results for an ambiguous query and provides a formal definition of the search result diversification problem.

Describes the emergence of the e-discovery field, identifies the information retrieval issues that arise, reviews the work to date on this topic, and summarises major open issues. This is an ideal primer for anyone with an interest in e-discovery.

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.

Surveys the research conducted and explains the methods and measures devised for evaluation of retrieval systems, including a detailed look at the use of statistical significance testing in retrieval experimentation.

Intellectual property and the patent system in particular have garnered a lot of attention, even in the public media, over the last few years. This monograph is not concerned with any of the controversial issues regarding the patent system itself but it does examine a very real and growing problem: searching for innovation.

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.

Provides an introduction to the field of learning to rank, a hot research topic in information retrieval and machine learning. Learning to Rank for Information Retrieval is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners.

The eruption of activity in the area of opinion mining and sentiment analysis has occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This book covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems.