Overview
The continued explosion in the amount of content
and the number for information sources available online is making the need
for effective personalized content delivery more acute. This has resulted
in a renewed interest in Web personalization as an indispensable tool for
Web-based organizations. Personalization can be defined as
any action that tailors the Web experience to a particular user, or set of
users. The experience can be something as casual as browsing a Web site or as
(economically) significant as trading stocks or purchasing a car. The actions
can range from simply making the presentation more pleasing to anticipating the
needs of a user and providing customized and relevant information. To achieve
effective personalization, organizations must rely on all available data,
including the usage and click-stream data (reflecting user behaviour), the
site content, the site structure, domain knowledge, as well as user
demographics and profiles. In addition, efficient and intelligent techniques
are needed to mine this data for actionable knowledge, and to effectively use
the discovered knowledge to enhance the users' Web experience. These
techniques must address important challenges emanating from the size and the
heterogeneous nature of the data itself, as well as the dynamic nature of
user interactions with Web sites, especially in e-commerce applications.
These challenges include the scalability of the personalization solutions,
data integration, and successful integration of techniques from machine
learning, information retrieval and filtering, databases, knowledge
representation, data mining, text mining, statistics, and human-computer
interaction.
Throughout the history of the Web, AI has continued to play an
essential role in the development of Web-based information systems, and now
it is believed that e-commerce and specifically personalization will prove to
be the “killer-app” for AI. E-commerce and Web information systems
are rich sources of difficult problems and challenges for AI researchers.
This workshop intends to bring
together researchers and practitioners to foster an exchange of information
and ideas, and to facilitate a discussion of current and emerging topics
related to Web Intelligence. Web Intelligence exploits AI and advanced
information technology on the Web and
Internet, in order to gain business intelligence and to assist users.
The goal of the workshop is to stimulate the future
development of new models, methodologies, and tools for Web intelligence,
including effective and scalable Web-based personalization solutions and
recommender systems.
Topics
Original contributions are solicited in the following areas:
Data and Knowledge Modeling, Integration and Management
- Using domain knowledge for more effective personalization
- Data models for Web usage, content, and structure data
- Data integration across multiple channels
- Cognitive models for Web navigation and e-commerce interactions
- The role of user context
Quality of Service in Personalization
- Techniques for improving online data quality
- Evaluation of recommendation engines
- Metrics for personalization effectiveness
- Generation and updating of user profiles
Systems and Architectures
- Frameworks and systems for scalable collaborative filtering
- Agents for intelligent browsing and navigation
- Adaptive hypertext systems
- Hybrid Recommendation Systems
Enabling Technologies
- Machine learning and data mining in personalization
- Text mining techniques for content-based filtering
- Semantic Web mining
- Privacy preserving personalization
- Standards for data and knowledge modeling (e.g., PMML, RDF, CPEX,
DAML)
Submission Instructions
A title and abstract of the paper must be submitted by
March 14, 2003. Full papers must be submitted by 9pm GMT March 21,
2003. All submissions must be made electronically to
ss.anand@ulster.ac.uk.
Authors are required to submit a version of their full papers in
camera-ready format as prescribed by the IJCAI web site. Complete
instructions for formatting are available at the IJCAI-03 Web
site (http://www.ijcai-03.org).
Papers should be no more than 8 pages inclusive of all
references and figures. All papers must be submitted in either PDF (preferred)
or postscript. It is the responsibility of the authors to ensure that the
submitted papers print correctly on a variety of printers. If any special
fonts are used, they must be included in the submission. All papers must be
original, and have not been published or submitted elsewhere.
Note: Participants are expected to register for the main IJCAI conference
in addition to the workshop.
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Post-Workshop Book: In addition to the workshop
proceedings published by IJCAI, selected papers from the workshop will be
expanded and published as contributed chapters in a forthcoming book by
Springer as a volume in Lecture
Notes in Artificial Intelligence (LNAI). |
Important Dates and Deadlines
Abstract Submission: March 14, 2003 (extended deadline)
Full Paper Submission: March 21, 2003
Notification of Acceptance: April 20, 2003
Camera Ready Papers Due: May 23, 2003
Organizing Committee
Bamshad Mobasher (Co-Chair)
School of Computer Science, Telecommunication, and Information Systems,
DePaul University,
243 S. Wabash Ave.
Chicago, IL, 60604, USA
mobasher@cs.depaul.edu
Sarabjot Singh Anand (Co-Chair)
NIKEL, Faculty of Informatics
University of Ulster
Northern Ireland, UK
ss.anand@ulster.ac.uk
Program Committee
- Robert Cooley, KXEN., USA
- Asuman Dogac, METU, Turkey
- C. Lee Giles, Pennsylvania State University, USA
- Mark Levene, Birkbeck University of London, UK
- Jiming Liu, Hong Kong Baptist University, Hong Kong
- Maurice Mulvenna, NIKEL, Faculty of Informatics, University of Ulster, Northern Ireland, UK
- Dunja Mladanic, Josef Stefan Institute, Slovenia
- Enric Plaza, Institut d'Investigació en Intel.ligència Artificial, Catalonia (Spain)
- Francesco Ricci, ITC-irst, Italy
- Barry Smyth, University College Dublin, Ireland
- Myra Spiliopoulou, Leipzig Graduate School of Management, Germany
- Jaideep Srivastava, University of Minnesota, USA
- Alex Tuzhilin, Stern School of Business, New York University, USA
- Ning Zhong, Maebashi Institute of Technology, Japan
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