Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling

Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling

Edwin Simpson (HP Labs, UK) and Mark H. Butler (HP Labs, UK)
DOI: 10.4018/978-1-60566-306-7.ch003
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Abstract

The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of information resources. This chapter investigates methods for enabling improved navigation, user modeling, and personalization using collaboratively generated tags. The authors discuss the advantages and limitations of tags, and describe how relationships between tags can be used to discover latent structures that can automatically organize a collection of tags owned by a community. They give a hierarchical clustering algorithm for extracting latent structure and explain methods for determining tag specificity, then use visualization to examine latent structures. Finally the authors discuss future trends including using latent tag structures to create user models.
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Background

The Web is notable for its increasing abundance of information and the freedom it allows for new innovation, particularly the development of new methods to connect users with information and allow them to organize resources. These methods augment more established approaches such as free-text search (Gudivada et al., 1997; Page et al., 1998), directories and taxonomies (Garshol, 2004), explicit communication between users and subscriptions to news feeds or mailing lists. In this section we describe folksonomies and consider both the advantages and limitations of this approach. We then compare tagging with thesauri and controlled vocabularies, and consider how these techniques can be combined with tagging.

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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
$37.50
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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