CreChainDo: an iterative and interactive Web information retrieval system based on lattices

This review examines a sample of interactive information retrieval (IR) systems within the framework of a design challenge set forth by John Bennett in 1971. Bennett directed his challenge to a group of information scientists who attended The User Interface for Interactive Search of Bibliographic Data Bases Workshop. During the workshop, participants exchanged their insights and experiences on various aspects of user interface requirements for interactive IR systems and discussed system design features that could result in the design of more effective user interfaces for information retrieval. The main goals of this review are to characterize interactive IR design trends over time, examine the extent to which IR systems have responded to Bennett's design challenge, and to suggest potential future research directions and challenges.

Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR), where we would attempt to model and optimize an entire interactive retrieval process (rather than a single query) with consideration of many different ways a user can potentially interact with a search engine. This tutorial systematically reviews the progress of research in IIR with an emphasis on the most recent progress in the development of models, algorithms, and evaluation strategies for IIR. It starts with a broad overview of research in IIR and then gives an introduction to formal models for IIR using a cooperative game framework and covering decision-theoretic models such as the Interface Card Model and Probability Ranking Principle for IIR. Next, it provides a review of some representative specific techniques and algorithms for IIR, such as various forms of feedback techniques and diversification of search results, followed by a discussion of how an IIR system should be evaluated and multiple strategies proposed recently for evaluating IIR using user simulation. The tutorial ends with a brief discussion of the major open challenges in IIR and some of the most promising future research directions.

A general-purpose, interactive computerized information retrieval system specially suitable for the retrieval of bibliographic information from a small scale database. (DB) containing up to 15000 records is described. The user defines fields in terms of tags through the data definition processor which provides flexibility for the record structure. Another processor creates the DB from an off-line input file containing the tag(s) followed by the data for the field(s) supplied by the user. Retrieval of information is carried out by the query processor in an interactive manner. It provides options to (i) select a descriptor (the postings are displayed; (ii) combine 2 sets at a time; (iii) display the different sets chosen; and iv) print the chosen set(s) with full record details or specified tag fields only. The package is developed on an Usha-Alpha 1042 system using Alpha BASIC language under AMOS operating system.

There is need for more foundational research in the development of interactive information retrieval systems. The results of a week long discussion by a group of multi­disciplinary researchers have reported here. A brief description of main activities and major recommendations of the workshop are reported here.

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.

Over the last decade, architecture has witnessed a growing popularity for new computational tools such as parametric design environments (PDEs). Given their rapid evolution and development, expertise tends to become increasingly transient, and architects find themselves in a situation where they must constantly re-learn their tools. At the same time, access to information has become increasingly widespread. Self-learners can thus rely on information retrieval systems to address knowledge gaps. However, the inherent tool complexity has given rise to a new kind of knowledge. On the basis of the different types described by Anderson and Krathwohl, the authors have previously shown that conceptual knowledge is essential for teaching parametric design. In contrast, research on interactive information retrieval (IIR) has highlighted that procedural knowledge is preferred in create tasks like design. Consequently, it can be argued that in a self-learning situation, architects might not be adopting best practice in relation to knowledge retrieval, especially when considering the visual scripting nature of certain PDEs. The purpose of this paper is to observe cognitive patterns in knowledge search activities while designing in parametric environments and validate the integration of CLT and IIR for further research. We highlight the types of knowledge and sources architecture graduate students, novices in PDEs, search for during design over multiple sessions and why. The paper reports on three design tasks completed during a computational course that emphasized student's autonomy. A qualitative analysis of interviews reveals epistemic actions to fall prey to procedural information, which is in line with both IIR and CLT research. This research is part of a PhD project studying the impact of knowledge retrieval on architectural design when using PDEs. Eventually, it could raise awareness in education, research, and practice regarding information retrieval in architectural design.

The goal of interactive cross-lingual information retrieval systems is to support users in formulating effective queries and selecting the documents which satisfy their information needs regardless of the language of these documents. This dissertation aims at harnessing user-system interaction, extracting the added value and integrating it back into the system to improve cross-lingual information retrieval for successive users. To achieve this, user input at different interaction points will be evaluated. This will, among others, include interaction during user-assisted query translations, implicit and explicit relevance feedback and social tags. To leverage this input, explorative studies need to be conducted to determine beneficial user input and the methods of extracting it.

Videotex system is an interactive two-way information retrieval system providing a host of information from its vast database via the telephone network. It allows the subscriber to request information by dialling up the videotex computer; select pages of text and pictorial information and have them displayed on the screens of adapted home television sets.

This paper reports results of an evaluation study of MAP (Multi‐faceted Access to PubMed), a metadata induced query suggestion interface for PubMed bibliographic search.A novel evaluation methodology was used to address the challenges involved in evaluating an IIR (Interactive Information Retrieval) system such as the MAP interface. The most significant aspect of this methodology is that, instead of using assigned tasks common in traditional IR evaluation, it asks real users with real search requests to search with real systems in an experimental setting. Several performance measures were created based on which comparisons were made between MAP and PubMed baseline. MAP was shown to perform better in several of these measures, especially when the search requests had not been attempted before.The finding pointed to search characteristics as an important intervening variable in IIR evaluation. The advantages of and potential threats to our methodology were also discussed.

Toward Cranfield-inspired reusability assessment in interactive information retrieval evaluation

It is our great pleasure to welcome you to New Brunswick, New Jersey and the third ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2018). CHIIR, as you may know, has roots in two previously successful communities: The Information Interaction in Context (IIiX) conference and the Human Computer Information Retrieval (HCIR) symposium, which ran from 2006 and 2007 respectively. These meetings were focused on issues that involved Human Information Behavior (HIB), Interactive Information Retrieval (IIR) and Human-Computer Interaction (HCI). Those roots, as well as decades of research and development on information seeking and other forms of information interaction, human-centered design, and information retrieval are the backbone of CHIIR. At the same time, this vibrant new community has picked up more scholars and themes as it continues to attract new students and professionals, as well as emerging research topics that are evident in this year's program. In fact, CHIIR is one of the few forums that explicitly supports presentation of bold new ideas through its Perspective Papers category. In addition, we have picked up Demos as a new category for presentations this year. All of these wonderful things could not be possible without a large number of people making significant contributions. We are quite fortunate to have Katriina Byström, Falk Scholer, and Jeff Huang as our Program Chairs this year. These three brave souls took upon a tremendous effort of working with dozens of submissions, reviewers, and meta-reviewers and constructing a high-quality program while coordinating from three different continents. Soo Young Rieh and Preben Hansen, on the other hand dealt with higher than expected load for short papers and still managed to get them reviewed and assessed on time. Even our Doctoral Consortium (DC) this year ended up being quite challenging as we received twice as many submissions as before, but our fine DC chairs Jaceck Gwizdka and Vivek Singh found world-class mentors for these doctoral students. Thanks to our Workshops and Tutorials Chair Xiaojun Yuan, we have a fantastic lineup of tutorials on the first day and workshops on the last day. When the whole program was ready for the conference, it was our Proceedings Chair Roberto Gonzalez-Ibanez who managed to put together these proceedings that you are holding in your hands, or more likely viewing in the ACM Digital Library! We are thankful for having an energetic and resourceful local arrangement committee with Michael Cole as the Treasurer, Fernando Diaz as the Sponsorship Chair, Kaitlin Costello as the Local Organizing Chair, and Sunyoung Kim and Matt Mitsui as the Student Volunteers Chairs. And it was our Publicity Chair Anita Crescenzi, who helped us with not only the outreach efforts, but also with the registration system. Finally, none of these would have happened without the authors and reviewers from more than 30 countries whose contributions are reflected in every aspect of this conference. So, thank you all!

PurposeThis study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011–2020. It also examines changes in this structure from 2006–2010 through 2011–2015 to 2016–2020. Results will contribute to a better understanding of the information science research field.Design/methodology/approachThe well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011–2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor.FindingsThe two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011–2015 but shared dominance with information behavior during 2016–2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016–2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011–2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016–2020.Originality/valueAuthor co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.

Information retrieval in a semantic desktop environment is an important aspect that is to be stressed on. There are many works in the past which proposed many improved information retrieval techniques in this environment. But in most of them there is one thing that is lacking, is the involvement of user in the retrieval process. In other words it should be an interactive information retrieval system. This paper proposes an interaction based information retrieval system which interacts with the user to find out the hints and the suggestions from his/her side to get the best of the results which might satisfy the user.

There is a growing interest in the research on interactive information retrieval, particularly in the study of eye gaze-enhanced interaction. Feedback generated from user gaze features is important for developing an interactive information retrieval system. Generating these gaze features have become less difficult with the advancement of the eye tracker system over the years. In this work, eye movement as a source of relevant feedback was examined. A controlled user experiment was carried out and a set of documents were given to users to read before an eye tracker and rate the documents according to how relevant they are to a given task. Gaze features such as fixation duration, fixation count and heat maps were captured. The result showed a medium linear relationship between fixation count and user explicit ratings. Further analysis was carried out and three classifiers were compared in terms of predicting document relevance based on gaze features. It was found that the J48 decision tree classifier produced the highest accuracy.

Information retrieval has taken an important turn when the researchers started using user behavior to improve their ranking algorithms. With the advent of user behavior in information retrieval, the interactive information retrieval is beginning to make its mark. In this paper, we discuss how user behavior is being used in information retrieval. We survey various strategies used for incorporating user behavior into information retrieval. The key fact is that taking absolute feedback about whether the retrieved documents are relevant or not is very difficult, and if we can take the implicit feedback from the user in the form of user behavior, we can arrive at a better learning function for the algorithm. We, in this paper, would like to provide a case study of various approaches in using user behavior for information retrieval.