Mobile and Ubiquitous Information Access

We discuss how the wireless-mobile revolution will change the notion of relevance in information retrieval. We distinguish between classical relevance (e-relevance) and relevance for wireless/mobile information retrieval (w-relevance). Starting from a four-dimensional model of e-relevance previously developed by one of us, we discuss how, in an ubiquitous computing environment, much more information will be available, and how it is therefore likely that w-relevance will be more important than e-relevance to survive information overload. The similarities and differences between e-relevance and w-relevance are described, and we show that there are more differences than one might think at first. We specifically analyze the role that beyond-topical criteria have in the w-relevance case, and we show some examples to clarify and support our position.

[1]  Judy Kay,et al.  Iems: Helping Users Manage Email , 2003, User Modeling.

[2]  Candace L. Sidner,et al.  Email overload: exploring personal information management of email , 1996, CHI.

[3]  Wendy E. Mackay,et al.  Diversity in the use of electronic mail: a preliminary inquiry , 1988, TOIS.

[4]  Anoop Gupta,et al.  Supporting Email Workflow , 2001 .

[5]  Susan T. Dumais,et al.  A Bayesian Approach to Filtering Junk E-Mail , 1998, AAAI 1998.

[6]  Terry R. Payne,et al.  Interface Agents That Learn an Investigation of Learning Issues in a Mail Agent Interface , 1997, Appl. Artif. Intell..

[7]  George Buchanan,et al.  Improving mobile internet usability , 2001, WWW '01.

[8]  Jeffrey O. Kephart,et al.  Incremental Learning in SwiftFile , 2000, ICML.

[9]  Georgios Paliouras,et al.  An evaluation of Naive Bayesian anti-spam filtering , 2000, ArXiv.

[10]  William W. Cohen Learning Rules that Classify E-Mail , 1996 .

[11]  Lluís Màrquez i Villodre,et al.  Boosting Trees for Anti-Spam Email Filtering , 2001, ArXiv.

[12]  Stefano Mizzaro,et al.  Ephemeral and Persistent Personalization in Adaptive Information Access to Scholarly Publications on the Web , 2002, AH.

[13]  Carlo Tasso,et al.  A shell for developing non-monotonic user modeling systems , 1994, Int. J. Hum. Comput. Stud..

[14]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[15]  Carlo Tasso,et al.  User Model-Based Information Filtering , 1997, AI*IA.

[16]  Christopher Meek,et al.  Challenges of the Email Domain for Text Classification , 2000, ICML.

[17]  Jason D. M. Rennie ifile: An Application of Machine Learning to E-Mail Filtering , 2000 .

[18]  Judy Kay,et al.  Automatic Induction of Rules of e-mail Classification , 2001 .

[19]  Elio Masciari,et al.  Towards An Adaptive Mail Classifier , 2002 .

[20]  Patrick Pantel,et al.  SpamCop: A Spam Classification & Organisation Program , 1998, AAAI 1998.