Information Retrieval Systems

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

Purpose – People often engage in different information-seeking strategies (ISSs) within a single information-seeking episode. A critical concern for the design of information retrieval (IR) systems is how to provide support for these different behaviors in a manner which searchers can easily understand, navigate and use, as they move from one ISS to another. The purpose of this paper is to describe a dialogue structure that was implemented in an experimental IR system, in order to address this concern. Design/methodology/approach – The authors conducted a user-centered experiment to evaluate the IR systems. Participants were asked to search for information on two different task types, with four different topics per task, in both the experimental system and a baseline system emulating state-of-the-art IR systems. The authors report here the results related explicitly to the use of the experimental system's dialogue structure. Findings – For one of the task types, most participants followed the search steps as predicted in the dialogue structures, and those who did so completed the task in fewer moves. For the other task type, predicted order of moves was often not followed, but participants again used fewer moves when following the predicted order. Results demonstrate that the dialogue structures the authors designed indeed support effective human information behavior patterns in a variety of ways, and that searchers can effectively use a system which changes to support different ISSs. Originality/value – This study shows that it is both possible and beneficial, to design an IR system which can support multiple ISSs, and that such a system can be understood and used successfully.

Searching information retrieval systems is a highly interactive, iterative process that cannot be understood simply by comparing the output of a search session (the “search product”) to a query stated in advance. In this article, we examine evaluation goals and methods for studying information retrieval behavior, drawing examples from our own research and that of others. We limit our review to research that employs online monitoring, also known as transaction log analysis. Online monitoring is one of few methods that can capture detailed data on the search process at a reasonable cost; these data can be used to build quantitative models or to support qualitative interpretations of quantitative results. Monitoring is a data collection technique rather than a research design, and can be employed in experimental or field studies, whether alone or combined with other data collection methods. Based on the research questions of interest, the researcher must determine what variables to collect from each data source, which to treat as independent variables to manipulate, and which to treat as dependent variables to observe effects. Studies of searching behavior often treat search task and searcher characteristics as independent variables and may manipulate other independent variables specific to the research questions addressed. Search outcomes, time, and search paths frequently are treated as dependent variables. We discuss each of these sets of variables, illustrating them with sample results from the literature and from our own research. Our examples are drawn from the Science Library Catalog project, a 7-year study of children's searching behavior on an experimental retrieval system. We close with a brief discussion of the implications of these results for the design of information retrieval systems. © 1996 John Wiley & Sons, Inc.

Traditional information retrieval algorithms ignore user needs and are unable to obtain user gaze coordinates and gaze times, resulting in low retrieval accuracy. The author proposes a new interactive information retrieval algorithm for this purpose. Divide eye tracking technology evaluation indicators, visually process eye movement information, obtain user gaze coordinates and gaze time, and calculate the influence coefficients of each gaze area and each point in the area. Weighted visual words are accumulated to get a visual word list with the weight of the associated area, and visual word list and Rocchio algorithm are combined to build a hidden Relevance feedback retrieval model in semantic space to judge information retrieval preferences. Intelligent robot is introduced, and Jensen Shannon divergence is used to calculate the Kullback–Leibler divergence distance between the probability distributions of document sets, calculate the similarity matching, and complete the interactive information retrieval. The simulation results prove that, due to the introduction of intelligent robot strategy in this method, the amount of irrelevant retrieval information is reduced by matching user needs and retrieval results. Therefore, the amount of messages generated by information retrieval is significantly lower than that of the two literature methods, without adding additional network load. The network system is in a stable operation state, and users can quickly grasp their own required information, and the retrieval speed is also improved to a certain extent. It is proved that the proposed algorithm has high retrieval accuracy, can effectively reduce network load and achieve high-quality human-computer interactive information retrieval.

this paper provides an in-depth survey of challenges in the design of intelligent information retrieval systems, pointing out some similarities and differences in the core data mining and web based search operations. The procedures for evaluation of search engine performance and implicit feedback of the user with respect to a search result are studied with references to the different algorithms. We have proposed a novel neural satisfaction based feedback vector in contrast to the existing activity pattern based feedback as a future research direction in Intelligent IR. We addressed the select research work in the area of soft information retrieval using fuzzy sets, artificial neural networks, genetic algorithms and probabilistic information retrieval. As an instance of information retrieval, web mining is a set of operations to retrieve relevant documents from preprocessed, crawled and indexed web, and it can be categorized into more specialized tasks of web content mining, web structure mining and web usage mining. We have given a survey of the important reviews on topic of web mining and its associated tasks. On the basis of Identified challenges in information retrieval in general, and web mining in particular, we have concentrated on applicability of soft computing techniques and their hybrids in web mining, the performance related issues of select solutions, future of web mining and the next generation user's expectations from a search engine.

This article examines the structure and components of information storage and retrieval systems and information filtering systems. Analysis of the tasks performed in such selection systems leads to the identification of 13 components. Eight are necessarily present in all such systems, mechanized or not; the others may, but need not be, present. The authors argue that all selection systems can be represented in terms of combinations of these components. The components are of only two types: representations of data objects and functions that operate on them. Further, the functional components, or rules, reduce to two basic types: 1) transformation, making or modifying the members of a set of representations, and 2) sorting or partitioning. The representational transformations may be in the form of copies, excerpts, descriptions, abstractions, or mere identifying references. By partitioning, we mean dividing a set of objects by using matching, sorting, ranking, selecting, and other logically equivalent operations. The typical multiplicity of knowledge sources and of system vocabularies is noted. Some of the implications for the study, use, and design of information storage and retrieval systems are discussed.

Purpose Prior Information Retrieval (IR) research synthesizes progress from individual studies, yet academia-industry collaboration dynamics remain unexplored. This study investigates: (1) productivity patterns and venues, (2) citations-downloads relationships, (3) topic evolution, and (4) collaboration trends. Design/methodology/approach We perform an analysis of 53,471 ACM IR papers (2000–2018) using bibliometrics and DistilBERT topic modeling. Findings We find that industry-involved papers preferred WWW/CIKM venues; collaborations dominated RecSys/CSCW. We see that academia-industry collaborations achieved the highest download-to-citation conversion rates. Academia focused on algorithms; industry on applications; collaborations bridged both with rising human-centered themes. Research implications This is a pioneering large-scale bibliometrics revealing collaboration’s impact on IR knowledge evolution and provides a methodological framework for cross-sector analysis. Practical implications The paper identifies optimal venues (RecSys/CSCW) for partnerships and guides joint initiatives (shared datasets, grants) to bridge academia-industry divides and enhance research translation. Originality/value This is the first large-scale bibliometric analysis of IR academia-industry collaboration. The paper finds many novel insights, including the fact that collaboration boosts citation efficiency, enables complementary specialization, and drives topic convergence.

Key terms related to information searching and search models are defined. A historic context is provided to illustrate the evolution of the four main digital environments that users interact with in their search process to offer readers background information regarding the transition from manual information systems to computer-based information retrieval (IR) systems, as well as the transition from intermediary searching to end-user searching. Emphasis is placed on the review of different levels of information searching from search tactics/moves, search strategies, and usage patterns, to search models and associated factors in relation to task, user knowledge structure, IR system design, and social-organization context. Search models are further classified into two types, with one type illustrating information search process (ISP) and the other type emphasizing the factors that influence the process. In addition, unsolved problems and future research are discussed and suggested

Information Retrieval (IR) systems aim to retrieve data that satisfies certain requirements and constitute an important service in many types of networks, including Delay/Disruption Tolerant Networks (DTNs). In current DTN based IR systems, the data that satisfies a query is assumed to be stored on a single node. Therefore, once a node receives a query in which it has the corresponding data, the query can be resolved completely. However, in scenarios where a query requires data from multiple nodes, these IR systems may fail. Henceforth, in this paper, we propose Distributed Data-Centric Information Retrieval (DDC-IR), a data centric IR system that supports all query types; e.g., continuous and complex. More importantly, it is designed specifically to operate in DTNs. It also incorporates a new packet, aka Query Reply Packet, that includes both a query and one or more replies. We show how this packet facilitates efficient query resolution and enables data centric routing. In addition, it uses caching so that nodes store popular queries that has the effect of speeding up query resolution. We have conducted an extensive simulation study to compare DDC-IR to state of the art IR systems using the popular Random Waypoint model and a trace-file containing student movements on a campus. The results show that DDC-IR is able to resolve 50 % more queries and has an 80 % lower buffer occupancy level than existing IR systems. We also tested DDC-IR in networks with varying sizes. For networks with 100 nodes, DDC-IR is able to resolve queries while current IR systems fail to resolve any queries. In particular, when the number of nodes increases, current IR systems fail to resolve any queries, whilst DDC-IR is able to resolve complex and continuous queries. The influence of the number of sub-queries on query resolution time is also studied. Specifically, when the number of sub-queries in a complex query increases from five to nine, DDC-IR uses 50 % more time to resolve a query. In comparison, prior IR systems fail to resolve any queries.

Information Retrieval meets Large Language Models: A strategic report from Chinese IR community

This study was conducted at the Music Library of the Ministry of Information (ML-MOI) and the Music Library of the Cultural Affairs (ML-CA) of Eritrea. The main aim of the study was to designan appropriate Indigenous Music Information Storage and Retrieval System for Eritrea. A quantitative approach was mainly used to obtain data from a purposefully selected sample. The qualitative approach was also used in some research stages. Methods used included documentanalysis, questionnaire, personal interview, focus group discussion and observation. It was found that music data was recorded in a register, which also served as a finding aid. Users’ preference was to search for music by artist, title, producer, occasion, subject or language.Unfortunately this was not possible with the system that was being used since it only allowed searching through the name or initials of an artist. The paper recommends the establishment of an automated indigenous storage and retrieval system to enable registration of indigenous knowledge information, offering facilities for indexing, searching and report generation.

This article presents the performance evaluation of information retrieval system for English and Hindi cross-Indian languages. As India is a multilingual country, different varieties of data material in varieties of languages are increasing day by day on Internet database. And hence, multilingual information retrieval is big issue in country like India. Usually, people used their native dialect to represent their needs and communication, and hence, language diversity produces a great barricade. In this paper, a new research design of cross-lingual information retrieval system is proposed. Here, a new research design has been implemented for information retrieval based on English and Hindi cross-languages by applying an approach of query translation. English language is considered for the query language, and content is stored in the database in Hindi Language. Here, query translation from English language to Hindi language is done by using dictionary-based approach. Hindi WordNet is used for the query translation. Query expansion and query preprocessing methods are implemented to improve the performance of the new research design and accordingly get the good precision.KeywordsCLIRQuery translationInformation retrievalEnglish and Hindi languages

This article discusses a multiple search session model of end-users' interaction with information retrieval systems based on results from an exploratory study investigating end-users' search sessions over time with online public access catalogs (OPAC) or CD-ROM databases at different stages of their information seeking related to a current research project. Interviews were conducted with 200 academic end-users to investigate the occurrence of multiple search sessions. Results show that at the time of the interview, 57% of end-users had conducted multiple search sessions during their research project and 86% of end-users conducted their first search session at the beginning stage of their information-seeking process. Forty-nine percent of end-users had conducted between 1 and 6 search sessions and 8% more than 6 search sessions. Seventy percent of multiple search session end-users' had modified their search terms since their first search session. The implications of the findings for end-user training, information retrieval systems design, and further research are discussed. © 1996 John Wiley & Sons, Inc.

Relevance is an essential concept in Information Retrieval (IR). Recent studies using brain imaging have significantly contributed towards the understanding of this concept, but only as a binary notion, i.e. a document being judged as relevant or non-relevant. While such a binary division is prevalent in IR, seminal theories have proposed relevance as a graded variable; i.e. having different degrees. In this paper, we aim to investigate the brain activity associated with relevance when it is treated as a graded concept. Twenty-five participants provided graded relevance judgements in the context of a Question Answering (Q/A) Task, during assessment with an electroencephalogram (EEG). Our findings show that significant differences in event-related potentials (ERPs) were observed in response to information segments processed in the context of high-relevance, low-relevance and no-relevance, supporting the concept of graded relevance. We speculate that differences in attentional engagement, semantic mismatch (between the question and answer) and memory processing underpin the electrophysiological responses to the graded relevance judgements. We believe our conclusions constitute an important step in unravelling the nature of graded relevance and knowledge of the electrophysiological modulation to each grade of relevance will help to improve the design and evaluation of IR systems.

INTRODUCTION:Increasing numbers of research papers about information retrieval for Health Technology Assessments (HTA), systematic reviews and other evidence syntheses are being published. It is time-consuming for information specialists to keep up-to-date with the latest developments in the field. To help searchers with this challenge, the Interest Group on Information Retrieval (IRG) of Health Technology Assessment International (HTAi) has compiled the best available research evidence on information retrieval aspects into an open-access web resource: Summarized Research in Information Retrieval for HTA (SuRe Info). The resource can be accessed at http://www.sure-info.orgMETHODS:The Sure Info team run topic-specific search strategies in selected relevant databases to identify information retrieval methods publications that fulfil the SuRe Info inclusion criteria. Eligible publications receive a structured abstract containing a brief critical appraisal. Key messages for search practice based on the appraisals and accepted best practice are summarized into topic-specific chapters.RESULTS:SuRe Info currently offers fourteen chapters, with more in development. SuRe Info chapters fall into two categories: (i) chapters about general search methods that are used across all types of research, such as how to develop search strategies and the availability and use of search filters, and (ii) chapters summarizing the methods to use when searching for specific aspects of HTA (as defined in the European Network for HTA (EUnetHTA) HTA Core Model®), including searching for evidence on clinical effectiveness and safety, and identifying economic evaluations. References at the end of each chapter are linked to appraisals of publications that have been used to develop each chapter. Links to the full-text of the publications are provided when freely available. The SuRe Info chapters are reviewed every six months and updated if new evidence is identified or if resources change.CONCLUSIONS:SuRe Info is a unique resource, identifying and summarizing current best research evidence on information retrieval aspects for HTA. It supports the timely uptake of potential efficiencies arising from new evidence that may be incorporated into the evidence identification processes of HTA organizations.