A Comparative Analysis of Personalization Techniques for a Mobile Application

In order to adapt to the environment of the user, devices have to be able to deduce the user's goals and information needs. In mobile environments, the goals and information needs of the user potentially depend on the user's situation. Existing work on context-dependent user modeling has mainly focused on specific application domains, most notably location-based services such as tourist guides, or on technological enablers. What is currently lacking is an understanding of when and why different personalization techniques work or fail. In this paper, we compare different classification algorithms on data collected from a mobile application. Our results show that methods that are able to learn tree-structured dependencies seem good candidates for personalization due to (i) the inherent hierarchical nature of context information and (ii) the fast running time of the algorithms. We also suggest two future research issues: (1) obtaining a better understanding of the nature of dependencies in contextual data, and (2) using collaborative user modeling techniques to improve the predictive power of user models.

[1]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[2]  Hong Joo Lee,et al.  Context-Aware Recommendations on the Mobile Web , 2005, OTM Workshops.

[3]  Jadwiga Indulska,et al.  Personalising Context-Aware Applications , 2005, OTM Workshops.

[4]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[5]  Eric Horvitz,et al.  Bayesphone: Precomputation of Context-Sensitive Policies for Inquiry and Action in Mobile Devices , 2005, User Modeling.

[6]  Keith Cheverst,et al.  A Survey of Map-based Mobile Guides , 2005 .

[7]  Fabien L. Gandon,et al.  Ambient Intelligence: The MyCampus Experience , 2005 .

[8]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[9]  Anders Kofod-Petersen.  Agnar Aamodt: Case-based situation assessment in a mobile context-aware system , 2003 .

[10]  David A. Bell,et al.  Learning Bayesian networks from data: An information-theory based approach , 2002, Artif. Intell..

[11]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[12]  Roy H. Campbell,et al.  A Middleware for Context-Aware Agents in Ubiquitous Computing Environments , 2003, Middleware.

[13]  Antonio Krüger,et al.  A User Modeling Markup Language (UserML) for Ubiquitous Computing , 2003, User Modeling.

[14]  Mahadev Satyanarayanan,et al.  Pervasive computing: vision and challenges , 2001, IEEE Wirel. Commun..

[15]  Eric Horvitz,et al.  The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users , 1998, UAI.

[16]  Johan Koolwaaij,et al.  Context Watcher ─ Sharing context information in everyday life , 2006 .

[17]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[18]  Andreas Krause,et al.  SenSay: a context-aware mobile phone , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[19]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[20]  Eemil Lagerspetz,et al.  A System for Context-Dependent User Modeling , 2006, OTM Workshops.

[21]  Jon Orwant,et al.  Heterogeneous learning in the Doppelgänger user modeling system , 2005, User Modeling and User-Adapted Interaction.

[22]  Johan Koolwaaij,et al.  Context-Aware Recommendations in the Mobile Tourist Application COMPASS , 2004, AH.

[23]  Ingrid Zukerman,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Predictive Statistical Models for User Modeling , 1999 .