Web Personalization can be defined as any set of actions that can tailor 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 clickstream data (reflecting user behaviour), the site content, the site structure, domain knowledge, as well as user demographics and profiles. 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 of the data, the fact that they are heterogeneous and very personal in nature, as well as the dynamic nature of user interactions with the Web. These challenges include the scalability of the personalization solutions, data integration, and successful integration of techniques from machine learning, information retrieval and filtering, databases, agent architectures, knowledge representation, data mining, text mining, statistics, information security and privacy, user modelling and human-computer interaction.

Recommender systems represent one special and prominent class of such personalized Web applications, which particularly focus on the user-dependent filtering and selection of relevant information and – in an e-Commerce context - aim to support online users in the decision-making and buying process. Recommender Systems have been a subject of extensive research in AI over the last decade, but with today's increasing number of e-commerce environments on the Web, the demand for new approaches to intelligent product recommendation is higher than ever. There are more online users, more online channels, more vendors, more products and, most importantly, increasingly complex products and services. These recent developments in the area of recommender systems generated new demands, in particular with respect to interactivity, adaptivity, and user preference elicitation. These challenges, however, are also in the focus of general Web Personalization research.

In the face of this increasing overlap of the two research areas, the aim of this workshop is to bring together researchers and practitioners of both fields, to foster an exchange of information and ideas, and to facilitate a discussion of current and emerging topics related to "Web Intelligence", particularly regarding its application in recommender systems. This workshop represents the sixth in a successful series of ITWP workshops that have been held at IJCAI and AAAI and would be – after the successful event on AAAI'07 - the 2nd workshop to join topics in Web personalization and Recommender Systems.  
 

User Modeling

• The use of domain knowledge and ontologies in user modeling
• User context modeling and algorithms for contextual and task-oriented recommender systems
• Individual and Group user models / Group recommendation
• Cross-Domain Models
• Privacy and Trust / Robust and trust-aware recommender systems.
• Automated Techniques for user profile generation and updating
• Cognitive and sociological models for Web navigation, e-commerce interactions, and recommendation
• Self-adaptation

• Catering to user privacy preferences
• Knowledge acquisition strategies
• The role of domain knowledge in preference elicitation
• Utility function elicitation from Implicit and Explicit user interaction
• Data models for Web usage, content, and structure data
• Integration of content, structure and usage data for preference discovery
• Techniques for improving online data quality
• Latent factor mining
• Natural language interaction and explanations

Architectures and Systems

• Personalized Search
• Scalability of personalization and recommendation techniques
• Agents for intelligent browsing and navigation
• Adaptive hypertext systems
• Privacy-preserving personalization methods
• Architectures for personalized privacy
• Hybrid Recommendation System
• Conversational Recommendation Systems

• Data/Web mining for personalization
• Link Analysis and Graph Mining
• Automated techniques for ontology generation, learning, and acquisition
• Machine Leaning techniques for information extraction and integration
• Learning metadata and Harvesting
• Web 2.0 and the Semantic Web
• Ubiquitous Environments

Evaluation Methodologies, Metrics, and Case Studies

• User Studies
• Empirical Evaluation of systems and metrics
• Practical applications and case studies

All submissions must be made electronically to the workshop organizers (see below).  Please use the AAAI Press prescribed formatting instructions available at http://www.aaai.org/Publications/Author/author.php. Papers should be no longer than 12 pages inclusive of all references and figures. All papers must be submitted in PDF. All papers must be original, and must not have not been published or submitted elsewhere.  At least one author for each accepted paper is expected to attend the workshop.

The workshop proceedings will be published by AAAI Press as part of the AAAI Technical Report Series and will be available on AAAI Digital Library. Selected papers from the workshop may be considered for expansion and inclusion in a special issue of a Journal.