Understanding patrons’ micro-level information seeking (MLIS) in information retrieval situations

2005 ◽  
Vol 41 (4) ◽  
pp. 929-947 ◽  
Author(s):  
Mei-Mei Wu
Keyword(s):  
Information Retrieval ◽  
Information Seeking ◽  
Level Information ◽  
Micro Level

scholarly journals A Dynamic Methodology for Improving the Search Experience

2006 ◽  
Vol 25 (2) ◽  
pp. 78 ◽  
Author(s):  
Marcia D. Kerchner
Keyword(s):  
Information Retrieval ◽  
Systems Engineering ◽  
Information Seeking ◽  
Business Owners ◽  
Early Years ◽  
Search Performance ◽  
Working Relationship ◽  
Help Desk ◽  
Modern Information

In the early years of modern information retrieval, the fundamental way in which we understood and evaluated search performance was by measuring precision and recall. In recent decades, however, models of evaluation have expanded to incorporate the information-seeking task and the quality of its outcome, as well as the value of the information to the user. We have developed a systems engineering-based methodology for improving the whole search experience. The approach focuses on understanding users’ information-seeking problems, understanding who has the problems, and applying solutions that address these problems. This information is gathered through ongoing analysis of site-usage reports, satisfaction surveys, Help Desk reports, and a working relationship with the business owners.

scholarly journals Towards Supporting Community Information Seeking and Use

2007 ◽  
Vol 3 (1) ◽  
Author(s):  
Nkechi Nnadi ◽  
Michael Gurstein
Keyword(s):  
Information Retrieval ◽  
Information Seeking ◽  
Design Guidelines ◽  
Community Context ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Community Information ◽  
Nature Of Information ◽  
Single User ◽  
Collaborative Information Retrieval

In this paper we explore issues surrounding the design of systems that will effectively support community information seeking and use. We discuss the need for information retrieval systems to move away for the single user paradigm to one that recognizes the collaborative nature of information seeking and use. We also examine the collaborative information retrieval literature and derive implications for community informatics. The paper also explores the unique challenges of designing systems to support information seeking and use in a community context, and attempts to provide design guidelines that will enable researchers and practitioners to develop such systems.

Information Retrieval Technologies and the “Realities” of Music Information Seeking

2016 ◽  
pp. 102-121
Author(s):  
Charilaos Lavranos ◽  
Petros Kostagiolas ◽  
Joseph Papadatos
Keyword(s):  
Information Retrieval ◽  
Information Seeking ◽  
Research Area ◽  
Music Technology ◽  
Information Seeking Behavior ◽  
Seeking Behavior ◽  
Evolution Of Music ◽  
Music Information ◽  
Search And Retrieval ◽  
The Way

Music information seeking incorporates the human activities that are carried out for the search and retrieval of music information. In recent years, the evolution of music technology holds a central role affecting the nature of music information seeking behavior. The research area that deals with the accessibility and the retrievability process of music information is known as Music Information Retrieval (MIR). This chapter focuses on the presentation of MIR technologies which has a direct impact in the way that individuals, as well as different music communities such as composers, performers, listeners, musicologists, etc., handle and utilize music information. The aim of this chapter is to investigate the way different music communities interact with MIR systems. Our approach is based on a selected literature review regarding the MIR systems and the information seeking behavior of the musicians.

Conclusions and Future Directions

2008 ◽  
pp. 334-347
Author(s):  
Iris Xie
Keyword(s):  
Information Retrieval ◽  
Digital Libraries ◽  
Information Seeking ◽  
Web Search ◽  
Full Range ◽  
Relevant Information ◽  
Public Access ◽  
Communication Process ◽  
Online Databases ◽  
Digital Environments

The emergence of the Internet has allowed millions of people to use a variety of electronic information retrieval (IR) systems, such as digital libraries, Web search engines, online databases, and Online Public Access Catalogues (OPACs). The nature of IR is interaction. Interactive information retrieval is defined as the communication process between the users and the IR systems. However, the dynamics of interactive IR is not yet fully understood. Moreover, most of the existing IR systems do not support the full range of users’ interactions with IR systems. Instead, they only support one type of information-seeking strategy: how to specify queries by using terms to select relevant information. However, new digital environments require users to apply multiple information-seeking strategies and shift from one information- seeking strategy to another in the information retrieval process.

Interactive IR Models

2008 ◽  
pp. 183-214 ◽  
Author(s):  
Iris Xie
Keyword(s):  
Information Retrieval ◽  
Information Seeking ◽  
Cognitive Model ◽  
Interaction Process ◽  
Information Need ◽  
User Input ◽  
Seeking Behavior ◽  
Nature Of Information ◽  
System Output ◽  
Stratified Model

The nature of information retrieval (IR) is interaction. However, the traditional IR model only focuses on the comparison between user input and system output. It does not illustrate the changeable interaction process (Saracevic, 1997). The human involvement of IR makes the process complicated and dynamic. Belkin (1993) further identified the two underlying assumptions of the traditional IR view: (1) The information need is static, and can be specified; and (2) there is only one form of information-seeking behavior. The limitations of the traditional IR model are becoming more evident. In the 1990s researchers started to develop interactive IR models. Among them, Ingwersen’s cognitive model (1992, 1996), Belkin’s episode model of interaction with texts (1996), and Saracevic’s stratified model (1996a, 1997) are the most cited ones.

Modeling Users for Adaptive Information Retrieval by Capturing User Intent

2011 ◽  
pp. 88-118 ◽  
Author(s):  
Eugene Santos Jr. ◽  
Hien Nguyen
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Information Seeking ◽  
Query Language ◽  
Traditional Approach ◽  
User Model ◽  
Evaluation Methodology ◽  
User Intent ◽  
Document Frequency ◽  
Frequency Weighting

In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a user’s intent is captured and used for improving his/her effectiveness in an information seeking task. The user intent is captured by analyzing the commonality of the retrieved relevant documents. The effectiveness of our user model is evaluated with regards to retrieval performance using an evaluation methodology which allows us to compare with the existing approaches from the information retrieval community while assessing the new features offered by our user model. We compare our approach with the Ide dec-hi approach using term frequency inverted document frequency weighting which is considered to be the best traditional approach to relevance feedback. We use CRANFIELD, CACM and MEDLINE collections which are very popular collections from the information retrieval community to evaluate relevance feedback techniques. The results show that our approach performs better in the initial runs and works competitively with Ide dec-hi in the feedback runs. Additionally, we evaluate the effects of our user modeling approach with human analysts. The results show that our approach retrieves more relevant documents to a specific analyst compared to keyword-based information retrieval application called Verity Query Language.

Intelligent Information Personalization

2011 ◽  
pp. 118-146 ◽  
Author(s):  
Syed Sibte Raza Abidi
Keyword(s):  
Information Retrieval ◽  
User Model ◽  
Functional Requirements ◽  
Future Outlook ◽  
Web Based ◽  
Level Information ◽  
Intelligent Information ◽  
Intelligent Methods ◽  
Traditional Approaches ◽  
Information Personalization

This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based on an individual’s interests, needs and goals. We present intelligent techniques to dynamically compose new personalized information by adapting existing web-based information in line with a dynamic user-model, whilst simultaneously addressing linguistic, factual and functional requirements. This chapter will highlight the different facets, tasks and issues concerning intelligent information personalization to guide researchers in designing intelligent information personalization applications. The chapter presents intelligent methods that address information personalization at the content level as opposed to the traditional approaches that focus on interface level information personalization. To assist researchers in designing intelligent information personalization applications we present our information personalization framework, named AdWISE (Adaptive Webmediated Information and Services Environment), to demonstrate how to systematically integrate various intelligent methods to achieve information personalization. We will conclude with a commentary on the future outlook for intelligent information personalization.

Seeking and sharing information dialogically

2019 ◽  
Vol 75 (3) ◽  
pp. 530-549
Author(s):  
Reijo Savolainen
Keyword(s):  
Conversation Analysis ◽  
Information Seeking ◽  
Design Methodology ◽  
Asynchronous Discussion ◽  
Content Type ◽  
Information Interaction ◽  
Discussion Threads ◽  
Micro Level ◽  
Do It Yourself ◽  
Discussion Topic

Purpose Drawing on the ideas of conversation analysis (CA), the purpose of this paper is to elaborate the picture of dialogical information seeking and sharing. To this end, information seeking and sharing are approached as interactive online talk occurring in an asynchronous discussion forum. Design/methodology/approach The conceptual framework is based on the elaboration of Schegloff’s model for sequence organisation in spoken conversation. As a result, ten categories constitutive of asynchronous online talk were identified. It was further examined how online talk of this type is structured by expanded question – answers adjacency pairs and how such pairs are constitutive of dialogical information seeking and sharing. This question was explored by scrutinising 20 discussion threads downloaded from a do-it-yourself related online forum. Findings Four ideal typical patterns of asynchronous online talk were identified. Answering the question is a basic pattern of online talk, based on the provision of responses to an individual request. Specifying the answer, broadening the discussion topic and challenging the answer represent more sophisticated patterns incorporating post-expansions of diverse kind. Research limitations/implications As the study focusses on four patterns constitutive of online talk occurring in a particular domain, the findings cannot be generalised to depict the phenomena of dialogical information interaction as a whole. Further research is needed to scrutiny the particular features of asynchronous online talk in the context of dialogical information interaction. Originality/value The paper pioneers by examining the potential of CA in the micro-level study of dialogical information seeking and sharing structured by expanded adjacency pairs. The findings also identify the limitations of the conversation analytic methodology in the study of asynchronous online discourse.

Sign in / Sign up

Export Citation Format

Share Document