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Information retrieval is the foundation for modern search engines. This textbook offers an introduction to the core topics underlying modern search technologies, including algorithms, data structures, indexing, retrieval, and evaluation. The emphasis is on implementation and experimentation; each chapter includes exercises and suggestions for student projects. Wumpus -- a multiuser open-source information retrieval system developed by one of the authors and available online -- provides model implementations and a basis for student work. The modular structure of the book allows instructors to use it in a variety of graduate-level courses, including courses taught from a database systems perspective, traditional information retrieval courses with a focus on IR theory, and courses covering the basics of Web retrieval. In addition to its classroom use, Information Retrieval will be a valuable reference for professionals in computer science, computer engineering, and software engineering.

The presented ontology-based model for indexing and retrieval combines the methods and experiences of traditional indexing languages with their cognitively interpreted entities and relationships with the strengths and possibilities of formal knowledge representation. The core component of the model uses inferences along the paths of typed relations between the entities of a knowledge representation for enabling the determination of result sets in the context of retrieval processes. A proposal for a general, but condensed, inventory of typed relations is given. The entities are arranged in aspect-oriented facets to ensure a consistent hierarchical structure. The possible consequences for indexing and retrieval are discussed.

Gross et al. (2015) have demonstrated that about a quarter of hits would typically be lost to keyword searchers if contemporary academic library catalogs dropped their controlled subject headings. This article re- ports on an investigation of the search value that subject descriptors and identifiers assigned by professional indexers add to a bibliographic database, namely the Australian Education Index (AEI). First, a similar methodology to that developed by Gross et al. (2015) was applied, with keyword searches representing a range of educational topics run on the AEI database with and without its subject indexing. The results indicated that AEI users would also lose, on average, about a quarter of hits per query. Second, an alternative research design was applied in which an experienced literature searcher was asked to find resources on a set of educational topics on an AEI database stripped of its subject indexing and then asked to search for additional resources on the same topics after the subject indexing had been reinserted. In this study, the proportion of additional resources that would have been lost had it not been for the subject indexing was again found to be about a quarter of the total resources found for each topic, on average.

With the increasing number and diversity of search tools available, interest in the evaluation of search systems, particularly from a user perspective, has grown among researchers. More researchers are designing and evaluating interactive information retrieval (IIR) systems and beginning to innovate in evaluation methods. Maturation of a research specialty relies on the ability to replicate research, provide standards for measurement and analysis, and understand past endeavors. This article presents a historical overview of 40 years of IIR evaluation studies using the method of systematic review. A total of 2,791 journal and conference units were manually examined and 127 articles were selected for analysis in this study, based on predefined inclusion and exclusion criteria. These articles were systematically coded using features such as author, publication date, sources and references, and properties of the research method used in the articles, such as number of subjects, tasks, corpora, and measures. Results include data describing the growth of IIR studies over time, the most frequently occurring and cited authors and sources, and the most common types of corpora and measures used. An additional product of this research is a bibliography of IIR evaluation research that can be used by students, teachers, and those new to the area. To the authors' knowledge, this is the first historical, systematic characterization of the IIR evaluation literature, including the documentation of methods and measures used by researchers in this specialty.

Purpose – Development of an effective search system and interface largely depends on usability studies. The aim of this paper is to present the results of an empirical evaluation of a prototype web site search and browsing tool based on multidimensional taxonomies derived from the use of faceted classification. Design/methodology/approach – A prototype Faceted Classification System (FCS), which classifies and organizes web documents under different facets (orthogonal sets of categories), was implemented on the domain of an academic institute. Facet are created from content oriented metadata, and then assembled into multiple taxonomies that describe alternative classifications of the web site content, such as by subject and location. The search and browsing interfaces use these taxonomies to enable users to access information in multiple ways. The paper compares the FCS interfaces to the existing single‐classification system to evaluate the usability of the facets in typical navigation and searching tasks. Findings – The findings suggest that performance and usability are significantly better with the FCS in the areas of efficient access, search success, flexibility, understanding of content, relevant search result, and satisfaction. These results are especially promising since unfamiliarity often leads users to reject new search interfaces. Originality/value – The results of the study in this paper can significantly contribute to interface research in the IR community, emphasizing the advantages of multidimensional taxonomies in online information collections.

Purpose – The aim of this article is to estimate the impact of faceted classification and the faceted analytical method on the development of various information retrieval tools over the latter part of the twentieth and early twenty‐first centuries. Design/methodology/approach – The article presents an examination of various subject access tools intended for retrieval of both print and digital materials to determine whether they exhibit features of faceted systems. Some attention is paid to use of the faceted approach as a means of structuring information on commercial web sites. The secondary and research literature is also surveyed for commentary on and evaluation of facet analysis as a basis for the building of vocabulary and conceptual tools. Findings – The study finds that faceted systems are now very common, with a major increase in their use over the last 15 years. Most LIS subject indexing tools (classifications, subject heading lists and thesauri) now demonstrate features of facet analysis to a greater or lesser degree. A faceted approach is frequently taken to the presentation of product information on commercial web sites, and there is an independent strand of theory and documentation related to this application. There is some significant research on semi‐automatic indexing and retrieval (query expansion and query formulation) using facet analytical techniques. Originality/value – This article provides an overview of an important conceptual approach to information retrieval, and compares different understandings and applications of this methodology.

Purpose – This paper aims to provide an overview of principles and procedures involved in creating a faceted classification scheme for use in resource discovery in an online environment. Design/methodology/approach – Facet analysis provides an established rigorous methodology for the conceptual organization of a subject field, and the structuring of an associated classification or controlled vocabulary. This paper explains how that methodology was applied to the humanities in the FATKS project, where the objective was to explore the potential of facet analytical theory for creating a controlled vocabulary for the humanities, and to establish the requirements of a faceted classification appropriate to an online environment. A detailed faceted vocabulary was developed for two areas of the humanities within a broader facet framework for the whole of knowledge. Research issues included how to create a data model which made the faceted structure explicit and machine-readable and provided for its further development and use. Findings – In order to support easy facet combination in indexing, and facet searching and browsing on the interface, faceted classification requires a formalized data structure and an appropriate tool for its management. The conceptual framework of a faceted system proper can be applied satisfactorily to humanities, and fully integrated within a vocabulary management system. Research limitations/implications – The procedures described in this paper are concerned only with the structuring of the classification, and do not extend to indexing, retrieval and application issues. Practical implications – Many stakeholders in the domain of resource discovery consider developing their own classification system and supporting tools. The methods described in this paper may clarify the process of building a faceted classification and may provide some useful ideas with respect to the vocabulary maintenance tool. Originality/value – As far as the authors are aware there is no comparable research in this area.

This study examined how searchers interact with a web-based, faceted library catalog when conducting exploratory searches. It applied multiple methods, including eye tracking and stimulated recall interviews, to investigate important aspects of faceted search interface use, specifically: (a) searcher gaze behavior—what components of the interface searchers look at; (b) how gaze behavior differs when training is and is not provided; (c) how gaze behavior changes as searchers become familiar with the interface; and (d) how gaze behavior differs depending on the stage of the search process. The results confirm previous findings that facets account for approximately 10–30% of interface use. They show that providing a 60-second video demonstration increased searcher use of facets. However, searcher use of the facets did not evolve during the study session, which suggests that searchers may not, on their own, rapidly apply the faceted interfaces. The findings also suggest that searcher use of interface elements varied by the stage of their search during the session, with higher use of facets during decision-making stages. These findings will be of interest to librarians and interface designers who wish to maximize the value of faceted searching for patrons, as well as to researchers who study search behavior.

This is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces. From parsing to indexing, clustering to classification, retrieval to ranking, and user feedback to retrieval evaluation, all of the most important concepts are carefully introduced and exemplified. The contents and structure of the book have been carefully designed by the two main authors, with individual contributions coming from leading international authorities in the field, including Yoelle Maarek, Senior Director of Yahoo! Research Israel; Dulce Poncele´on IBM Research; and Malcolm Slaney, Yahoo Research USA. This completely reorganized, revised and enlarged second edition of Modern Information Retrieval contains many new chapters and double the number of pages and bibliographic references of the first edition, and a companion website www.mir2ed.org with teaching material. It will prove invaluable to students, professors, researchers, practitioners, and scholars of this fascinating field of information retrieval.

First, a new model of searching in online and other information systems, called ‘berrypicking’, is discussed. This model, it is argued, is much closer to the real behavior of information searchers than the traditional model of information retrieval is, and, consequently, will guide our thinking better in the design of effective interfaces. Second, the research literature of manual information seeking behavior is drawn on for suggestions of capabilities that users might like to have in online systems. Third, based on the new model and the research on information seeking, suggestions are made for how new search capabilities could be incorporated into the design of search interfaces. Particular attention is given to the nature and types of browsing that can be facilitated.

This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user.

In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.

Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.

There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.

We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.

We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank.

This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.

This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.