AskAda is a conversational agent designed to lower barriers to accessing AI-driven tools and support university students in accessing authoritative scholarly resources for their academic activities. It enables topic identification and definition retrieval of appropriate terms to help students locate trustworthy and current scholarly information that is inaccessible as a commodity through public search engines. Operating within the WhatsApp platform, AskAda reduces the learning burden of adopting a new system and GUI and allows students to use the AI tool directly from a smartphone. Unlike many AI tools, AskAda emphasizes accountability and transparency that instructors can rely on and trust by validating the audit trails generated by the students’ search journey while completing their assignments. Two authors conducted an autobiographical study and evaluated the performance of AskAda based on two dimensions: efficiency and usability. The system facilitates topic identification and retrieval of scholarly resources from library databases while maintaining an auditable session history and fostering transparency in AI interaction in the educational context.

The International Workshop on NeuroPhysiological Approaches for Interactive Information Retrieval (NeuroPhysIIR'25) aims to bringing together researchers from information science, humancomputer interaction, cognitive neuroscience, and related fields, to foster cross-disciplinary collaboration and accelerate progress in neurophysiologically-informed IIR research.As the third edition following successful workshops at SIGIR'15 [5] and CHIIR'17 [6], we anticipate that the interactive nature of this workshop will not only raise awareness but also lower the entry barriers for engaging with this exciting research area within the wider IIR community.Workshop website: https://neurophysiir.github.io/chiir2025/.

Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

When it comes to emerging technologies, older adults are often those who can greatly benefit from the advancements but are vastly under-represented in research and designs. This study presents preliminary findings of older adults' search behavior with a spoken conversational search agent which represents the next generation search paradigm. Our findings show that, compared with their younger counterparts, older adults' search conversations lasted longer and included more requests. Their requests had greater length and tended to have a lower proportion of unique words, more grammatically complex sentences and short pauses. In addition, the older subjects preferred to start a request with "I" and request questions with modal verbs were less frequent. They reformulated spoken requests as competently as did younger adults but elaborations on requests were uniquely founded among older adults. They also tended to have more than one query or question in a single request and rephrasing requests was more frequently observed than younger adults. System implications and future research directions are discussed.

The workshop will be the second in a series, building upon a successful workshop held at the SIGIR 2015 conference. The main aim is to focus on a narrow but highly important set of topics that have been identified in the last workshop and are of importance to IR and IIR researchers. The core theme of the workshop will be challenges is using and applying neurophysiological experimental methodologies and their applicability and adaptation in the context of IR and IIR research studies. The goal will be to clarify some of the major neurophysiological methodological concepts and constructs relevant to IR and IIR, expand awareness of critical parameters associated with neurophysiological devices and equipment, and provide a foundation to researchers to enable them to apply appropriate neurophysiological modalities for specific types of IR and IIR research investigations. Discussions on establishing reference tasks and standard data sets and launching a special journal issue on key topics will round out the event.

We study decentralized searches in large-scale, self-organized peer-to-peer networks and investigate the influences of network size and degree distribution (neighborhood size) on search efficiency. Experimental results show that searches are efficient and scalable in large networks, especially with large neighborhood sizes (degrees). Analysis of the data supports a proposed scalability model, in which search path length L (efficiency) is proportional to a poly-logarithmic function of network size N, with degree dm(majority neighborhood size) as the log base. The model explains 90% (R2) of variances in search path lengths. Search time (search path length) predicted by the model shows great potential for efficient searches in real-scale networks of up to a billion distributed systems.

In this paper, the potential for expanding the set of scientific evidence and insights associated with the users' role during the search process is explored. As it is intended to be a position paper and not a systematic survey, a comprehensive review of literature is not presented here. However, the authors draw on some early stage research, in this emerging area, to describe and explain the generation of neuro-physiological evidence using three types of modalities. The modalities and the associated methods described here, presented in order of increasing complexity, include Eye-tracking, EEG, and fMRI. The paper concludes with a few critical observations regarding the promises and perils of using neuro-physiological approaches in studying search and search behavior.

This Tutorial+Workshop will discuss opportunities and challenges involved in using neuro-physiological tools/techniques (such as fMRI, fNIRS, EEG, eye-tracking, GSR, HR, and facial expressions) and theories in information retrieval. The hybrid format will engage researchers and students at different levels of expertise, from those who are active in this area to those who are interested and want to learn more. The workshop will combine presentations, discussions and tutorial elements and consist of four segments (tutorial, completed research, work-in-progress, closing panel).

With the ubiquitous production, distribution and consumption of information, today's digital environments such as the Web are increasingly large and decentralized. It is hardly possible to obtain central control over information collections and systems in these environments. Searching for information in these information spaces has brought about problems beyond traditional boundaries of information retrieval (IR) research. This article addresses one important aspect of scalability challenges facing information retrieval models and investigates a decentralized, organic view of information systems pertaining to search in large-scale networks. Drawing on observations from earlier studies, we conduct a series of experiments on decentralized searches in large-scale networked information spaces. Results show that how distributed systems interconnect is crucial to retrieval performance and scalability of searching. Particularly, in various experimental settings and retrieval tasks, we find a consistent phenomenon, namely, the Clustering Paradox , in which the level of network clustering (semantic overlay) imposes a scalability limit. Scalable searches are well supported by a specific, balanced level of network clustering emerging from local system interconnectivity. Departure from that level, either stronger or weaker clustering, leads to search performance degradation, which is dramatic in large-scale networks.

It is crucial to study basic principles that support adaptive and scalable retrieval functions in large networked environments such as the Web, where information is distributed among dynamic systems. We conducted experiments on decentralized IR operations on various scales of information networks and analyzed effectiveness, efficiency, and scalability of various search methods. Results showed network structure, i.e., how distributed systems connect to one another, is crucial for retrieval performance. Relying on partial indexes of distributed systems, some level of network clustering enabled very efficient and effective discovery of relevant information in large scale networks. For a given network clustering level, search time was well explained by a poly-logarithmic relation to network size (i.e., the number of distributed systems), indicating a high scalability potential for searching in a growing information space. In addition, network clustering only involved local self-organization and required no global control - clustering time remained roughly constant across the various scales of networks.

We proposed and implemented a novel clustering algorithm called LAIR2, which has constant running time average for on-the-fly Scatter/Gather browsing [4]. Our experiments showed that when running on a single processor, the LAIR2 on-line clustering algorithm was several hundred times faster than a parallel Buckshot algorithm running on multiple processors [11]. This paper reports on a study that examined the effectiveness of the LAIR2 algorithm in terms of clustering quality and its impact on retrieval performance. We conducted a user study on 24 subjects to evaluate on-the-fly LAIR2 clustering in Scatter/Gather search tasks by comparing its performance to the Buckshot algorithm, a classic method for Scatter/Gather browsing [4]. Results showed significant differences in terms of subjective perceptions of clustering quality. Subjects perceived that the LAIR2 algorithm produced significantly better quality clusters than the Buckshot method did. Subjects felt that it took less effort to complete the tasks with the LAIR2 system, which was more effective in helping them in the tasks. Interesting patterns also emerged from subjects' comments in the final open-ended questionnaire. We discuss implications and future research.

This paper describes an application of IR and text categorization methods to a highly practical problem in biomedicine, specifically, Gene Ontology (GO) annotation. GO annotation is a major activity in most model organism database projects and annotates gene functions using a controlled vocabulary. As a first step toward automatic GO annotation, we aim to assign GO domain codes given a specific gene and an article in which the gene appears, which is one of the task challenges at the TREC 2004 Genomics Track. We approached the task with careful consideration of the specialized terminology and paid special attention to dealing with various forms of gene synonyms, so as to exhaustively locate the occurrences of the target gene. We extracted the words around the gene occurrences and used them to represent the gene for GO domain code annotation. As a classifier, we adopted a variant of k-Nearest Neighbor (kNN) with supervised term weighting schemes to improve the performance, making our method among the top-performing systems in the TREC official evaluation. Moreover, it is demonstrated that our proposed framework is successfully applied to another task of the Genomics Track, showing comparable results to the best performing system.

Abstract Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large‐scale information infrastructure, e.g., digital libraries. To provide large‐scale information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single‐agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium‐scale applications. In this article, we discuss two distributed (multi‐agent) information filtering approaches, that are distributed with respect to knowledge or functionality, to overcome the limitations of single‐agent centralized information filtering. Large‐scale experimental studies involving the well‐known TREC data set are also presented to illustrate the advantages of distributed filtering as well as to compare the different distributed approaches.

Abstract There has been considerable interest in recent years in providing automated information services, such as information classification, by means of a society of collaborative agents. These agents augment each other's knowledge structures (e.g., the vocabularies) and assist each other in providing efficient information services to a human user. However, when the number of agents present in the society increases, exhaustive communication and collaboration among agents result in a large communication overhead and increased delays in response time. This paper introduces a method to achieve selective interaction with a relatively small number of potentially useful agents, based on simple agent modeling and acquaintance lists. The key idea presented here is that the acquaintance list of an agent, representing a small number of other agents to be collaborated with, is dynamically adjusted. The best acquaintances are automatically discovered using a learning algorithm, based on the past history of collaboration. Experimental results are presented to demonstrate that such dynamically learned acquaintance lists can lead to high quality of classification, while significantly reducing the delay in response time.

We investigate the modeling of changes in user interest in information filtering systems. A new technique for tracking user interest shifts based on a Bayesian approach is developed. The interest tracker is integrated into a profile learning module of a filtering system. We present an analytical study to establish the rate of convergence for the profile learning with and without the user interest tracking component. We examine the relationship among degree of shift, cost of detection error, and time needed for detection. To study the effect of different patterns of interest shift on system performance we also conducted several filtering experiments. Generally, the findings show that the Bayesian approach is a feasible and effective technique for modeling user interest shift.

The goal of this research is to clarify the role of document classification in information filtering. An important function of classification, in managing computational complexity, is described and illustrated in the context of an existing filtering system. A parameter called classification homogeneity is presented for analyzing unsupervised automated classification by employing human classification as a control. Two significant components of the automated classification approach, vocabulary discovery and classification scheme generation, are described in detail. Results of classification performance revealed considerable variability in the homogeneity of automatically produced classes. Based on the classification performance, different types of interest profiles were created. Subsequently, these profiles were used to perform filtering sessions. The filtering results showed that with increasing homogeneity, filtering performance improves, and, conversely, with decreasing homogeneity, filtering performance degrades.

In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream must be handled efficiently. In this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties. A filtering system, SIFTER, has been implemented based on the model, using established techniques in information retrieval and artificial intelligence. These techniques include document representation by a vector-space model, document classification by unsupervised learning, and user modeling by reinforcement learning. The system can filter information based on content and a user's specific interests. The user's interests are automatically learned with only limited user intervention in the form of optional relevance feedback for documents. We also describe experimental studies conducted with SIFTER to filter computer and information science documents collected from the Internet and commercial database services. The experimental results demonstrate that the system performs very well in filtering documents in a realistic problem setting.

Article Free Access Share on Detection of shifts in user interests for personalized information filtering Authors: W. Lam Department of Management Sciences, S306 Pappajohn Building, The University of Iowa, Iowa City, Iowa Department of Management Sciences, S306 Pappajohn Building, The University of Iowa, Iowa City, IowaView Profile , S. Mukhopadhyay Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, IN Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, INView Profile , J. Mostafa School of Library and Information Science, Indiana University, Bloomington, IN School of Library and Information Science, Indiana University, Bloomington, INView Profile , M. Palakal Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, IN Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, INView Profile Authors Info & Claims SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrievalAugust 1996 Pages 317–325https://doi.org/10.1145/243199.243279Published:18 August 1996Publication History 36citation915DownloadsMetricsTotal Citations36Total Downloads915Last 12 Months40Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF