A Case Study of User Behavior in Information Retrieval

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.

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!

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.

The information science research community is characterized by a paradigm split, with a system‐centered cluster working on information retrieval (IR) algorithms and a user‐centered cluster working on user behavior. The two clusters rarely leverage each other's insight and strength. One major suggestion from user‐centered studies is to treat the relevance judgment of documents as a subjective, multidimensional, and dynamic concept rather than treating it as objective and based on topicality only. This study explores the possibility to enhance users' topicality‐based relevance judgment with subjective novelty judgment in interactive IR. A set of systems is developed which differs in the way the novelty judgment is incorporated. In particular, this study compares systems which assume that users' novelty judgment is directed to a certain subtopic area and those which assume that users' novelty judgment is undirected. This study also compares systems which assume that users judge a document based on topicality first and then novelty in a stepwise, noncompensatory fashion and those which assume that users consider topicality and novelty simultaneously and as compensatory to each other. The user study shows that systems assuming directed novelty in general have higher relevance precision, but systems assuming a stepwise judgment process and systems assuming a compensatory judgment process are not significantly different.

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.

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.

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

Most books on information-seeking neglect the human side to focus solely on the technical aspects of retrieval systems. This book is the first to provide an information science perspective on information retrieval, with a focus on user behavior and needs. Beginning with the history of information retrieval, coverage goes on to consider behavioral models and interactive techniques as well as the design, implementation and evaluation of information retrieval interfaces. This comprehensive book will be the go-to for LIS students and professionals hoping to better appreciate and utilize information retrieval systems, theories, and practices. Contents Interactive information retrieval : history and background / Colleen Cool and Nicholas J. Belkin Information behavior and seeking / Peiling Wang Task-based information searching and retrieval / Elaine G. Toms Approaches to investigating information interaction and behaviour / Raya Fidel Information representation / Mark D. Smucker Access models / Edie Rasmussen Evaluation / Kalervo Jarvelin Interfaces for information retrieval / Max Wilson Interactive techniques / Ryen W. White Web retrieval, ranking and personalization / Jaime Teevan and Susan Dumais Recommendation, collaboration and social search / David M. Nichols and Michael B. Twidale Multimedia : behaviour, interfaces and interaction / Haiming Liu, Suzanne Little and Stefan Ruger Multimedia : information representation and access / Suzanne Little, Evan Brown and Stefan Ruger

E-commerce platforms usually train their recommender system models to achieve personalized recommendations based on user behavior data. User behavior can be categorized into implicit and explicit feedback. Explicit feedback data have been well studied. However, the implicit feedback data still have many issues, such as the multiple types of behavior data, lack of negative feedback, and lack of the ability to express the real user preference. Targeting these problems of implicit feedback, we propose a TDF-WNSP-WLFM (time decay factor-weight of negative sample possibility-weighted latent factor model) based on the latent factor model for product recommendation. Our method mainly focuses on reconstructing the implicit rating matrix to enable the algorithm to perform better. The TDF-WNSP-WLFM algorithm is tested on two public user behavior datasets from Taobao and REES46, two big e-commerce platforms. Our algorithm compares favorably with other known collaborative filtering methods.

In this paper discussed about information seeking behavior of library users of the State University of Padang Postgraduate Library because of a lack of knowledge in finding the information they need precisely and quickly. Because not a few postgraduate users who feel confused in finding the information needed. This research question covers how post-market visitors' information seeking behavior encompasses the stages of their activities. The purpose of writing this paper is to describe the behavior of information retrieval of postgraduate library students at Padang State University. The method of data collection is carried out on librarians and librarians through observation, interviews, and library studies in the Postgraduate Library of Padang State University. Analyzing data is done descriptively.Based on analyzing the data, it was concluded that in searching for library information in the Postgraduate Library of Padang State University there are two stages, namely: (1) behavior of the user when searching for information with the user strategy in information retrieval, which includes: starting, browsing, and extracting, (2) meanwhile, the technique of searching information on librarians in the Postgraduate Library of Padang State University shows that in conducting information search, library users use information search techniques based on words and information search techniques based on the closest words.Keywords: optimization, competence, electronic archives.

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.

We present a novel approach to detect misuse within an information retrieval system by gathering and maintaining knowledge of the behavior of the user rather than anticipating attacks by unknown assailants. Our approach is based on building and maintaining a profile of the behavior of the system user through tracking, or monitoring of user activity within the information retrieval system. Any new activity of the user is compared to the user profile to detect a potential misuse for the authorized user. We propose four different methods to detect misuse in information retrieval systems. Our experimental results on $2$ GB collection favorably demonstrate the validity of our approach.

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

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.

A new interactive system for computerized taxonomic identification and information retrieval, operable on MS-DOS microcomputers, is now available. In this application to southern African grass genera the data include synonyms, morphology, anatomy, physiology, cytology, classification, pathogens, world and local distributions and references. The program is part of the DELTA system, which also provides natural language descriptions and printed keys. The data for the southern African genera can be provided separately, or included in the complete data for the grass genera of the world. ’n Nuwe interaktiewe stelsel vir gerekenariseerde taksonomiese identifisering en inligtingsherwinning, bedryf-baar op MS-DOS-mikrorekenaars, is nou beskikbaar. Hierdie toepassing op Suider-Afrikaanse grasgenusse bevat data oor sinonieme, morfologie, anatomie, fisiologie, sitologie, klassifikasie, patogene, wereldwye en plaaslike verspreidings en verwysings. Die program is deel van die DELTA-stelsel, wat ook voorsiening maak vir beskrywings in gewone taal en gedrukte sleutels. Die data vir die Suider-Afrikaanse genusse kan afsonderlik verskaf word, of by die volledige data vir die grasgenusse van die wereld ingesluit word.