Intelligent Techniques for Web Personalization & Recommender Systems
|Start||July 11th 2009 (iCal)|
|End||July 11th 2009|
|Papers due:||March 15th 2009|
|Submissions due:||March 15th 2009|
|Notification:||April 17th 2009|
|Camera ready due:||May 08th 2009|
The 7th Workshop on Intelligent Techniques for Web Personalization & Recommender Systems
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 click-stream 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". This workshop represents the seventh in a successful series of ITWP workshops that have been held at IJCAI and AAAI since 2001 and would be – after the successful events at AAAI'07 and AAAI'08 - the 3rd workshop on ITWP and Recommender Systems.