Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment

Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment

Colum Foley (Dublin City University, Ireland), Alan F. Smeaton (Dublin City University, Ireland) and Gareth J.F. Jones (Dublin City University, Ireland)
DOI: 10.4018/978-1-60566-306-7.ch007
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Abstract

Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an information need. Recent advances in both Web technologies, such as the sociable Web of Web 2.0, and computer hardware, such as tabletop interface devices, have enabled multiple users to collaborate on many computer-related tasks. Due to these advances there is an increasing need to support two or more users searching together at the same time, in order to satisfy a shared information need, which we refer to as Synchronous Collaborative Information Retrieval. Synchronous Collaborative Information Retrieval (SCIR) represents a significant paradigmatic shift from traditional IR systems. In order to support an effective SCIR search, new techniques are required to coordinate users’ activities. In this chapter we explore the effectiveness of a sharing of knowledge policy on a collaborating group. Sharing of knowledge refers to the process of passing relevance information across users, if one user finds items of relevance to the search task then the group should benefit in the form of improved ranked lists returned to each searcher.In order to evaluate the proposed techniques the authors simulate two users searching together through an incremental feedback system. The simulation assumes that users decide on an initial query with which to begin the collaborative search and proceed through the search by providing relevance judgments to the system and receiving a new ranked list. In order to populate these simulations we extract data from the interaction logs of various experimental IR systems from previous Text REtrieval Conference (TREC) workshops.
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Introduction

The phrase “Collaborative Information Retrieval” has been used in the past to refer to many different technologies which support collaboration in the information retrieval (IR) process. Much of the early work in collaborative information retrieval has been concerned with asynchronous, remote collaboration via the reuse of previous search results and processes in collaborative filtering systems, collaborative re-ranking, and collaborative footprinting systems. Asynchronous collaborative information retrieval supports a passive, implicit form of collaboration where the focus is to improve the search process for an individual.

Synchronous collaborative information retrieval (SCIR) is an emerging form of collaborative IR in which a group of two or more users are explicitly collaborating in a synchronised manner in order to satisfy a shared information need. The motivation behind these systems is related to both the ever-growing corpus of human knowledge on the web, the improvement of social awareness on the internet today, and the development of novel computer interface devices. SCIR systems represent a significant paradigmatic shift in focus and motivation compared with traditional IR systems and asynchronous collaborative IR systems. The development of new IR techniques is needed to exploit this. In order for collaborative IR to be effective there needs to be both an appropriate division of labour, and an effective sharing of knowledge across collaborating searchers (Zeballos, 1998; Foley et al., 2006). Division of labour enables each collaborating group member to explore a subset of a document collection in order to reduce the redundancy associated with multiple people viewing the same documents. Sharing of knowledge enables collaborating users to benefit from the knowledge of their collaborators. Early SCIR systems provided various awareness cues such as chat windows, shared whiteboards and shared bookmarks. By providing these cues, these systems enabled the collaborating searchers to coordinate their activities in order to achieve a division of labour and sharing of knowledge. However, coordinating activities amongst users can be troublesome, requiring too much cognitive load (Adcock et al., 2007).

Recently we have seen systems to support a more system-mediated division of labour by dividing the results of a search query amongst searchers (Morris and Horvitz, 2007), or defining searcher roles (Adcock et al., 2007). However, there has been no work to date which addresses the system-mediated sharing of knowledge across collaborating searchers. In this chapter we introduce our techniques to allow for effective system-mediated sharing of knowledge. We evaluate how a sharing of knowledge policy affects the performance of a group of users searching together collaboratively. But first, in the next section, we provide a comprehensive account of work to date in synchronous collaborative information retrieval.

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Synchronous Collaborative Information Retrieval

Information retrieval (IR), as defined by Baeza-Yates and Ribeiro-Neto (1999), is concerned with the representation, storage, organisation of and access to information items. The purpose of an IR system is to satisfy an information need.

Synchronous collaborative information retrieval (SCIR) systems are concerned with the realtime, explicit, collaboration which occurs when multiple users search together to satisfy a shared information need; these systems represent a significant paradigmatic shift in IR systems from an individual focus to a group focus. As such these systems represent a more explicit, active form of collaboration, where users are aware that they are collaborating with others towards a common, and usually explicitly stated, goal. This collaboration can take place either remotely, or, in a co-located setting. These systems have gained in popularity and now with the ever-growing popularity of the social web (or Web 2.0), support for explicit, synchronous collaborative information retrieval is becoming more important than ever.

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Table of Contents
Foreword
Bamshad Mobasher
Acknowledgment
Max Chevalier, Christine Julien, Chantal Soule-Dupuy
Chapter 1
Laurent Candillier, Kris Jack, Françoise Fessant, Frank Meyer
The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative... Sample PDF
State-of-the-Art Recommender Systems
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Chapter 2
Neal Lathia
Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people... Sample PDF
Computing Recommendations with Collaborative Filtering
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Chapter 3
Edwin Simpson, Mark H. Butler
The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of... Sample PDF
Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling
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Chapter 4
Adaptive User Profiles  (pages 65-87)
Steve Cayzer, Elke Michlmayr
A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant... Sample PDF
Adaptive User Profiles
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Chapter 5
Eugene Santos Jr., Hien Nguyen
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... Sample PDF
Modeling Users for Adaptive Information Retrieval by Capturing User Intent
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Chapter 6
Mihaela Brut, Florence Sedes, Corinne Zayani
Inside the e-learning platforms, it is important to manage the user competencies profile and to recommend to each user the most suitable documents... Sample PDF
Ontology-Based User Competencies Modeling for E-Learning Recommender Systems
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Chapter 7
Colum Foley, Alan F. Smeaton, Gareth J.F. Jones
Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an... Sample PDF
Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment
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Chapter 8
Charles Delalonde, Eddie Soulier
This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The... Sample PDF
DemonD: A Social Search Engine Built Upon the Actor-Network Theory
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Chapter 9
Hager Karoui
In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference... Sample PDF
COBRAS: Cooperative CBR Bibliographic Recommender System
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Chapter 10
Zehra Cataltepe, Berna Altinel
As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative... Sample PDF
Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity
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Chapter 11
Nima Taghipour, Ahmad Kardan
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender... Sample PDF
Web Content Recommendation Methods Based on Reinforcement Learning
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Chapter 12
Angela Carrillo-Ramos, Manuele Kirsch Pinheiro, Marlène Villanova-Oliver, Jérôme Gensel, Yolande Berbers
The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is... Sample PDF
Collaborating Agents for Adaptation to Mobile Users
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Chapter 13
Cristina Gena, Liliana Ardissono
This chapter describes the user-centered design approach we adopted in the development and evaluation of an adaptive Web site. The development of... Sample PDF
A User-Centered Approach to the Retrieval of Information in an Adaptive Web Site
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Chapter 14
Antonella Carbonaro, Rodolfo Ferrini
Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently... Sample PDF
Personalized Information Retrieval in a Semantic-Based Learning Environment
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Chapter 15
Hanh Huu Hoang, Tho Manh Nguyen, A Min Tjoa
Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding... Sample PDF
A Semantic Web Based Approach for Context-Aware User Query Formulation and Information Retrieval
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