Your search

Results 47 resources

  • 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.

  • As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model supported by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world.

  • In this paper we describe a general framework for evaluation and optimization of methods for diversifying query results. In these methods, an initial ranking candidate set produced by a query is used to construct a result set, where elements are ranked with respect to relevance and diversity features, i.e., the retrieved elements should be as relevant as possible to the query, and, at the same time, the result set should be as diverse as possible. While addressing relevance is relatively simple and has been heavily studied, diversity is a harder problem to solve. One major contribution of this paper is that, using the above framework, we adapt, implement and evaluate several existing methods for diversifying query results. We also propose two new approaches, namely the Greedy with Marginal Contribution (GMC) and the Greedy Randomized with Neighborhood Expansion (GNE) methods. Another major contribution of this paper is that we present the first thorough experimental evaluation of the various diversification techniques implemented in a common framework. We examine the methods' performance with respect to precision, running time and quality of the result. Our experimental results show that while the proposed methods have higher running times, they achieve precision very close to the optimal, while also providing the best result quality. While GMC is deterministic, the randomized approach (GNE) can achieve better result quality if the user is willing to tradeoff running time.

  • We live in an information age that requires us, more than ever, to represent, access, and use information. Over the last several decades, we have developed a modern science and technology for information retrieval, relentlessly pursuing the vision of a "memex" that Vannevar Bush proposed in his seminal article, "As We May Think." Faceted search plays a key role in this program. Faceted search addresses weaknesses of conventional search approaches and has emerged as a foundation for interactive information retrieval. User studies demonstrate that faceted search provides more effective information-seeking support to users than best-first search. Indeed, faceted search has become increasingly prevalent in online information access systems, particularly for e-commerce and site search. In this lecture, we explore the history, theory, and practice of faceted search. Although we cannot hope to be exhaustive, our aim is to provide sufficient depth and breadth to offer a useful resource to both researchers and practitioners. Because faceted search is an area of interest to computer scientists, information scientists, interface designers, and usability researchers, we do not assume that the reader is a specialist in any of these fields. Rather, we offer a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth.

  • In studying actual Web searching by the public at large, we analyzed over one million Web queries by users of the Excite search engine. We found that most people use few search terms, few modified queries, view few Web pages, and rarely use advanced search features. A small number of search terms are used with high frequency, and a great many terms are unique; the language of Web queries is distinctive. Queries about recreation and entertainment rank highest. Findings are compared to data from two other large studies of Web queries. This study provides an insight into the public practices and choices in Web searching.

  • In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such.

  • The objectives of the study were to conduct a series of observations and experiments under as real-life situation as possible related to: (1) user context of questions in information retrieval; (2) the structure and classification of questions; (3) cognitive traits and decision making of searchers; and (4) diferent searches of the same question. The study is presented in three parts: Part I presents the background of the study and describes the models, measures, methods, procedures and statistical analyses used. Part II is devoted to results related to users, questions and effectiveness measures, and Part III to results related to searchers, searches and overlap studies. A concluding summary of all results is presented in Part III. © 1988 John Wiley & Sons, Inc.

  • All is flux. —Plato on Knowledge in the Theaetetus (about 369 BC) Relevance is a, if not even the, key notion in information science in general and information retrieval in particular. This two-part critical review traces and synthesizes the scholarship on relevance over the past 30 years or so and provides an updated framework within which the still widely dissonant ideas and works about relevance might be interpreted and related. It is a continuation and update of a similar review that appeared in 1975 under the same title, considered here as being Part I. The present review is organized in two parts: Part II addresses the questions related to nature and manifestations of relevance, and Part III addresses questions related to relevance behavior and effects. In Part II, the nature of relevance is discussed in terms of meaning ascribed to relevance, theories used or proposed, and models that have been developed. The manifestations of relevance are classified as to several kinds of relevance that form an interdependent system of relevancies. In Part III, relevance behavior and effects are synthesized using experimental and observational works that incorporated data. In both parts, each section concludes with a summary that in effect provides an interpretation and synthesis of contemporary thinking on the topic treated or suggests hypotheses for future research. Analyses of some of the major trends that shape relevance work are offered in conclusions.

  • The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared.

  • 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.

  • The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970–1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters.

  • The goal of the Redundancy, Diversity, and Interdependent Document Relevance workshop was to explore how ranking, performance assessment and learning to rank can move beyond the assumption that the relevance of a document is independent of other documents. In particular, the workshop focussed on three themes: the effect of redundancy on information retrieval utility (for example, minimizing the wasted effort of users who must skip redundant information), the role of diversity (for example, for mitigating the risk of misinterpreting ambiguous queries), and algorithms for set-level optimization (where the quality of a set of retrieved documents is not simply the sum of its parts). This workshop built directly upon the Beyond Binary Relevance: Preferences, Diversity and Set-Level Judgments workshop at SIGIR 2008 [3], shifting focus to address the questions left open by the discussions and results from that workshop. As such, it was the first workshop to explicitly focus on the related research challenges of redundancy, diversity, and interdependent relevance – all of which require novel performance measures, learning methods, and evaluation techniques. The workshop program committee consisted of 15 researchers from academia and industry, with experience in IR evaluation, machine learning, and IR algorithmic design. Over 40 people attended the workshop. This report aims to summarize the workshop, and also to systematize common themes and key concepts so as to encourage research in the three workshop themes. It contains our attempt to summarize and organize the topics that came up in presentations as well as in discussions, pulling out common elements. Many audience members contributed, yet due to the free-flowing discussion, attributing all the observations to particular audience members is unfortunately impossible. Not all audience members would necessarily agree with the views presented, but we do attempt to present a consensus view as far as possible.

  • 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.

  • Relevance is a fundamental, though not completely understood, concept for documentation, information science, and information retrieval. This article presents the history of relevance through an exhaustive review of the literature. Such history being very complex (about 160 papers are discussed), it is not simple to describe it in a comprehensible way. Thus, first of all a framework for establishing a common ground is defined, and then the history itself is illustrated via the presentation in chronological order of the papers on relevance. The history is divided into three periods (“Before 1958,” “1959–1976,” and “1977–present”) and, inside each period, the papers on relevance are analyzed under seven different aspects (methodological foundations, different kinds of relevance, beyond-topical criteria adopted by users, modes for expression of the relevance judgment, dynamic nature of relevance, types of document representation, and agreement among different judges). © 1997 John Wiley & Sons, Inc.

  • 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.

  • 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 study examined how searchers interacted with a web-based, faceted library catalog when conducting exploratory searches. It applied eye tracking, stimulated recall interviews, and direct observation to investigate important aspects of gaze behavior in a faceted search interface: what components of the interface searchers looked at, for how long, and in what order. It yielded empirical data that will be useful for both practitioners (e.g., for improving search interface designs), and researchers (e.g., to inform models of search behavior). Results of the study show that participants spent about 50 seconds per task looking at (fixating on) the results, about 25 seconds looking at the facets, and only about 6 seconds looking at the query itself. These findings suggest that facets played an important role in the exploratory search process.

  • 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.

  • 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.

Last update from database: 3/28/24, 6:42 AM (UTC)