Interaction in information retrieval: Trends over time

This paper presents an iterative and interactive information retrieval (IR) system for Web search using formal concept analysis (FCA). FCA provides a natural way to organise objects according to their properties and it has been used in recent work to organise search engine results. The navigation over the lattice helps the user to explore a structured and synthetic result. Such a lattice contains concepts that are relevant and others that are not relevant regarding a given IR task. In this way, lattices are introduced in an interactive and iterative system. The user expresses his negative or positive agreement with some concept of the lattice in respect of his objective of IR. These user choices are converted into operations over the lattice. The lattice is dynamically updated for a better fit to the request.

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.

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.

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.

MESSIDOR is an interactive information retrieval system. It differs from current systems in that it allows the simultaneous search of several bibliographic databases. The databases may be on different sites and may use different query languages (MISTRAL, QUEST,...). These local languages are invisible to the users of MESSIDOR. They are all translated to a single language. We describe MESSIDOR's goals, system architecture, user language and some details of the implementation. The system is implemented on a MICRAL 80-30 microcomputer.

The NASA/RECON educational program in interactive information retrieval systems

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.

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.

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.

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.

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.

When evaluating interactive information retrieval (IIR) interfaces, it is common to collect data using subjective measures such as satisfaction, ease of use, usefulness, and user engagement. However, as these are collected post-task, they serve as surrogate measures for what occurred in the midst of the search activities. Further, such approaches may be subject to recency effects, where the last action in the search process influences the searchers’ opinions about the overall process. With recent improvements in facial emotion classification approaches, we propose that measuring emotional responses may provide a better indication of what is happening throughout search tasks. In this research, we present an approach for collecting real-time emotional responses during a search activity using consumer-grade front-facing cameras and a method of aligning these with search interface feature use. To validate the effectiveness of the approach, we have conducted a controlled laboratory study in which we manipulated the quality of the search results in order to determine if we can detect expected emotional responses, whether search behaviours influencing these emotional responses, and whether recency effects are present in post-task measures. The preliminary results of this study show that our approach is reliable for detecting emotional responses when searchers experience positive and negative emotions throughout the search process, isolate which interactive elements were used when positive and negative emotional responses were experienced, and illustrate how recency effects are present in post-task measures. Our upcoming study will investigate how our approach can be used to evaluate novel search interfaces. We will develop a novel search interface and evaluate it using our approach. Finally, we will create a dashboard to monitor academic literature. Using the same approach, we will demonstrate our approach can extend beyond traditional search interfaces and into more general interface assessment.