Web Personalization 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 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, content and 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 as well as the dynamic nature of user interactions with the Web. The challenges include the scalability of the personalization solutions, data integration, and the integration of techniques from machine learning, information retrieval and filtering, databases, agent systems, knowledge representation, data mining, text mining, statistics, information security and privacy, user modeling and human-computer interaction.
Recommender systems represent one special and prominent class of personalized Web applications, which focus on the user-dependent filtering and selection of relevant information and 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. The 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 light of the growing importance of these areas particularly in e-commerce environments and the 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 various current and emerging topics relevant to building personalized intelligent systems for the Web.