Adaptive User Profiles

Adaptive User Profiles

Steve Cayzer (Hewlett-Packard Laboratories, UK) and Elke Michlmayr (Vienna University of Technology, Austria)
DOI: 10.4018/978-1-60566-306-7.ch004
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A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant, personalized, and context-sensitive information delivery. Yet traditional approaches to user profiles rely on explicit, brittle models that go out of date very quickly, lack relevance, and have few natural connections to related models. In this chapter the authors show how it is possible to create adaptive user profiles without any explicit input at all. Rather, leveraging implicit behaviour on social information networks, the authors can create profiles that are both adaptive and socially connective. Such profiles can help provide personalized access to enterprise resources and help identify other people with related interests.
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There are many ways to deal with the challenges of collaborative knowledge management and discovery within enterprises. This chapter focuses on personalized, adaptive approaches, leveraging user behaviour on social information systems.

A major challenge for enterprise information systems is presenting the information that users want in a way that makes sense to them. In traditional approaches to information filtering, the user has to explicitly create his or her profile, and manually keep the profile up to date. Can we take advantage of the popularity of collaborative tagging systems, such as or, and use the recorded tagging behaviour to construct implicit, yet realistic and dynamic user profiles?

The use of profiles for personalization is not new, but such systems typically rely on an explicit, manually entered user profile. This imposes a burden on the user, both at initial creation time, and more importantly over time as the user’s skills and interests change, so the profile has to be updated. Typically, the created user profiles go out of date, fast.

Of course, this problem has been well understood for decades and much research has focused on the possibility of creating implicit user profiles. Put simply, such approaches attempt to ‘look over the user’s shoulder’ so to speak, and create a profile out of normal behaviour. The advantage with these approaches is that the mined profile should evolve simply and naturally with ongoing changes in user behaviour patterns.

There are some drawbacks with these approaches. It is, for example, difficult to mine accurate user profiles from observed behaviour. Another problem is dealing with the changing nature of user interests. How can one distinguish between long term characteristics (as for example defined by a user’s profession), medium term interests (such as ruby or agile_management for software engineers), and transient foci of attention (this year’s holiday planning, news articles)? How does one choose the right level of ‘forgetfulness’ in the user’s profile? A more subtle problem is that implicit user profiles are not examinable, or scrutable. Without some control over their profiles, users are likely to become distrustful of systems that use these profiles, particularly if they make egregious errors. While users do not want to spend excessive time doing ‘profile gardening’, they would like the facility to examine and tweak the profiles to correct errors or to proactively direct the system. A related issue is that of privacy: certainly on the public Internet, users are increasingly wary of the amount of information that is being gathered without their explicit consent

So we are in a situation where we would like to generate realistic, dynamic user profiles which are scrutable and privacy preserving. Where can we find such profiles? This chapter is primarily concerned with collaborative tagging systems, but this is just one possibility. Many of the principles discussed in this chapter are equally applicable to any system that a user interacts with on a regular basis. The use of folders in email, web browsing and document management is one possibility. User queries, both on the intra/internet and to enterprise systems, are another. Communities of interest, such as forums and mailing lists, provide yet another rich source of user behaviour to observe and to mine.

The basic operation of collaborative tagging systems is very simple. Users annotate a resource of interest, often a web page, with an arbitrary number of free text tags. These tags, personal or communal, can be used to browse a community’s resources, both documents and people. The popularity of such systems provides a useful store of personally identifiable user behaviour which can be used to create implicit user profiles. In this chapter we will survey related work on user profiles. Then, taking collaborative tagging systems as an exemplar of a source from which we can construct user profiles, we will present

  • 1.

    algorithms for creating such profiles

  • 2.

    approaches to profile analysis and evaluation

  • 3.

    methods for dynamic visualization of the generated profiles

  • 4.

    a discussion of the potentials of using such profiles for personalized access to enterprise data sources.

Complete Chapter List

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Table of Contents
Bamshad Mobasher
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
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
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
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
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
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
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
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
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
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
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
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
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
Chapter 14
Antonella Carbonaro, Rodolfo Ferrini
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Personalized Information Retrieval in a Semantic-Based Learning Environment
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
About the Contributors