Toward supporting information-seeking and retrieval activities based on evolving topic-needs

– Seeking and retrieving information is an essential aspect of knowledge workers' activities during problem‐solving and decision‐making tasks. In recent years, user‐oriented Information Seeking (IS) research methods rooted in the social sciences have been integrated with Information Retrieval (IR) research approaches based on computer science to capitalize on the strengths of each field. Given this background, the objective is to develop a topic‐needs variation determination technique based on the observations of IS&R theories., – In this study, implicit and explicit methods for identifying users' evolving topic‐needs are proposed. Knowledge‐intensive tasks performed by academic researchers are used to evaluate the efficacy of the proposed methods. The paper conducted two sets of experiments to demonstrate and verify the importance of determining changes in topic‐needs during the IS&R process., – The results in terms of precision and discounted cumulated gain (DCG) values show that the proposed Stage‐Topic_W (G,S) and Stage‐Topic‐Interaction methods can retrieve relevant document sets for users engaged in long‐term tasks more efficiently and effectively than traditional methods., – The improved precision of the proposed methods means that they can retrieve more relevant documents for the searcher. Accordingly, the results of this research have implications for enhancing the search function in enterprise content management (ECM) applications to support the execution of projects/tasks by professionals and facilitate effective ECM., – The model observes a user's search behavior pattern to determine the personal factors (e.g. changes in the user's cognitive status), and content factors (e.g. changes in topic‐needs) simultaneously. The objective is to capture changes in the user's information needs precisely so that evolving information needs can be satisfied in a timely manner.

[1]  Peter Ingwersen,et al.  Task complexity and information behaviour in group based problem solving , 2007, Inf. Res..

[2]  Duen-Ren Liu,et al.  Toward incorporating a task-stage identification technique into the long-term document support process , 2008, Inf. Process. Manag..

[3]  James A. Reimer,et al.  Enterprise Content Management , 2002, Datenbank-Spektrum.

[4]  David M. Nichols,et al.  Browsing is a collaborative process , 1997, Inf. Process. Manag..

[5]  Robin Burke,et al.  Inferring User’s Information Context from User Profiles and Concept Hierarchies , 2004 .

[6]  Peiling Wang Users' information needs at different stages of a research project: a cognitive view , 1997 .

[7]  T. D. Wilson,et al.  Models in information behaviour research , 1999, J. Documentation.

[8]  Micheline Beaulieu,et al.  Approaches to User-Based Studies in Information Seeking and Retrieval: A Sheffield Perspective , 2003, J. Inf. Sci..

[9]  C. J. van Rijsbergen,et al.  Incorporating user search behavior into relevance feedback , 2003, J. Assoc. Inf. Sci. Technol..

[10]  Karl Gyllstrom,et al.  A comparison of query and term suggestion features for interactive searching , 2009, SIGIR.

[11]  Xin Fu,et al.  Eliciting better information need descriptions from users of information search systems , 2007, Inf. Process. Manag..

[12]  Kalervo Järvelin,et al.  Task complexity affects information seeking and use , 1995 .

[13]  M. Lynne Markus,et al.  Toward A Theory of Knowledge Reuse : Types of Knowledge Reuse Situations and Factors in Reuse Success , 2022 .

[14]  Duen-Ren Liu,et al.  Task-based K-Support system: disseminating and sharing task-relevant knowledge , 2005, Expert Syst. Appl..

[15]  Analía Amandi,et al.  Modeling user interests by conceptual clustering , 2006, Inf. Syst..

[16]  Keiichiro Hoashi,et al.  Document filtering method using non-relevant information profile , 2000, SIGIR '00.

[17]  Pertti Vakkari,et al.  A theory of the task-based information retrieval process: a summary and generalisation of a longitudinal study , 2001, J. Documentation.

[18]  Ryen W. White,et al.  A study on the effects of personalization and task information on implicit feedback performance , 2006, CIKM '06.

[19]  Gerard Salton,et al.  Improving Retrieval Performance by Relevance Feedback , 1997 .

[20]  John Yen,et al.  Learning user interest dynamics with a three-descriptor representation , 2001 .

[21]  Philip Hider Search goal revision in models of information retrieval , 2006, J. Inf. Sci..

[22]  Hsinchun Chen,et al.  MetaSpider: Meta-searching and categorization on the Web , 2001, J. Assoc. Inf. Sci. Technol..

[23]  Jette Hyldegård,et al.  Beyond the search process - Exploring group members' information behavior in context , 2009, Inf. Process. Manag..

[24]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[25]  Amanda Spink,et al.  A study of mediated successive searching during information seeking , 1999, J. Inf. Sci..

[26]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[27]  T. D. Wilson,et al.  On user studies and information needs , 2006, J. Documentation.

[28]  Wolfgang Riggert,et al.  ECM - Enterprise Content Management , 2009 .

[29]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.

[30]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[31]  Gary Marchionini,et al.  Design of Interfaces for Information Seeking. , 1998 .

[32]  Pia Borlund,et al.  The IIR evaluation model: a framework for evaluation of interactive information retrieval systems , 2003, Inf. Res..

[33]  P Ingwersen Hyldegård, J., & Task complexity and information behaviour in group based problem solving. , 2007 .

[34]  Katriina Byström Municipal administrators at work—information needs and seeking [IN&S] in relation to task complexity: a case-study amongst municipal officials , 1997 .

[35]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part I. Background and Theory , 1997, J. Documentation.

[36]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[37]  Juhani Iivari,et al.  Characterizing the evolving research on enterprise content management , 2006, Eur. J. Inf. Syst..

[38]  Tom Wilson,et al.  Models in Information Behavior Research , 1999 .

[39]  Iris Xie Interactive Information Retrieval in Digital Environments , 2008 .

[40]  Marcia J. Bates,et al.  The design of browsing and berrypicking techniques for the online search interface , 1989 .

[41]  Daniel E. O'Leary,et al.  Enterprise Knowledge Management , 1998, Computer.

[42]  Nicholas J. Belkin,et al.  Evaluating interactive information retrieval systems: opportunities and challenges , 2004, CHI EA '04.

[43]  Peretz Shoval,et al.  Information Filtering: Overview of Issues, Research and Systems , 2001, User Modeling and User-Adapted Interaction.

[44]  Ian Ruthven,et al.  Interactive information retrieval , 2008 .

[45]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.

[46]  Peiling Wang,et al.  A cognitive model of document use during a research project. Study I. document selection , 1998 .

[47]  Tero Päivärinta,et al.  Enterprise Content Management: An Integrated Perspective on Information Management , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[48]  Anita Komlodi Task management support in information seeking: a case for search histories , 2004, Comput. Hum. Behav..

[49]  Carol Collier Kuhlthau,et al.  Seeking Meaning: a process approach to library and information services" Ablex Publishing , 2003 .

[50]  Pertti Vakkari,et al.  Changes of search terms and tactics while writing a research proposal: A longitudinal case study , 2003, Inf. Process. Manag..

[51]  Pertti Vakkari,et al.  Explanation in Information Seeking and Retrieval , 2005 .

[52]  Pertti Vakkari,et al.  Trends in ... a Critical Review: Growth of Theories on Information Seeking: An Analysis of Growth of a Theoretical Research Program on the Relation Between Task Complexity and Information Seeking , 1998, Inf. Process. Manag..

[53]  Tom Wilson,et al.  The development of user studies at Sheffield University, 1963-88 , 1988 .

[54]  Max L. Wilson A Transfer Report on the Development of a Framework to Evaluate Search Interfaces for their Support of Different User Types and Search Tactics , 2008 .

[55]  Ryen W. White,et al.  An implicit feedback approach for interactive information retrieval , 2006, Inf. Process. Manag..

[56]  William David Salisbury,et al.  Understanding the influence of organizational change strategies on information technology and knowledge management strategies , 2001, Decis. Support Syst..

[57]  Rafael Berlanga Llavori,et al.  Topic discovery based on text mining techniques , 2007, Inf. Process. Manag..

[58]  John Yen,et al.  Relevant data expansion for learning concept drift from sparsely labeled data , 2005, IEEE Transactions on Knowledge and Data Engineering.