Collaborative Information Retrieval: Concepts, Models and Evaluation

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.

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Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

Advances in Library and Information Science - Critical Approaches to Information Retrieval Research ◽

10.4018/978-1-7998-1021-6.ch002 ◽

2020 ◽

pp. 20-39

Author(s):

Fatiha Naouar ◽

Lobna Hlaoua ◽

Mohamed Nazih Omri

Keyword(s):

Information Retrieval ◽

Relevance Feedback ◽

Information Source ◽

Strong Association ◽

Relevant Information ◽

User Query ◽

Proposed Model ◽

Evidence Source ◽

Feedback Algorithm ◽

Collaborative Information Retrieval

Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results.

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DemonD

Social Computing ◽

10.4018/978-1-60566-984-7.ch042 ◽

2010 ◽

pp. 652-668

Author(s):

Charles Delalonde ◽

Eddie Soulier

Keyword(s):

Knowledge Management ◽

Information Retrieval ◽

Community Participation ◽

Search Engine ◽

Management Practices ◽

Actor Network Theory ◽

Social Search ◽

Retrieval Systems ◽

Knowledge Management Practices ◽

Collaborative Information Retrieval

This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The authors describe traditional knowledge management practices and review post-cognitivists theories in order to define social creation in collaborative information retrieval activity. The Actor-Network theory accurately describes association processes and includes both human and non-human entities. This chapter compares this theory with the emergence of Social Search services online and Experts’ Retrieval Systems. The chapter authors suggest afterward, a social search engine named DemonD that identifies documents but more specifically users relevant to a query. DemonD relies on transparent profile construction based upon user activity, community participation, and shared documents. Individuals are invited to participate in a dedicated newsgroup and the information exchanged is capitalized. The evaluation of our service both ergonomic and through a simulation provides encouraging data.

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Mastering Web Mining and Information Retrieval in the Digital Age

Advances in Data Mining and Database Management - Web Usage Mining Techniques and Applications Across Industries ◽

10.4018/978-1-5225-0613-3.ch001 ◽

2017 ◽

pp. 1-28 ◽

Cited By ~ 21

Author(s):

Kijpokin Kasemsap

Keyword(s):

Information Retrieval ◽

Organizational Performance ◽

User Interfaces ◽

Web Mining ◽

Digital Age ◽

Mass Storage ◽

Web Data ◽

Time Saving ◽

Storage Devices ◽

Collaborative Information Retrieval

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.

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