Intelligent Techniques for Web Personalization and Recommender Systems
Papers from the AAAI Workshop
Bamshad Mobasher, Sarabjot Singh Anand, Alfred Kobsa, and Dietmar Jannach, Cochairs
Web personalization aims at providing individual users or user groups with a web experience that is specifically tailored to them. 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.
To achieve effective personalization, a variety of types of data must be harnessed, including user profiles, web usage, content and structure, and domain knowledge. Efficient and intelligent techniques are needed to mine this data for actionable knowledge, and to effectively use the discovered knowledge to create user models. 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 the web. These challenges include the successful integration of techniques from machine learning, information retrieval and filtering, databases, agent architectures, knowledge representation, data mining, statistics, and user modeling.
This workshop represents the sixth in a successful series of workshops that have brought together researchers and practitioners to foster an exchange of ideas, and to facilitate a discussion of current and emerging topics related to web intelligence, web mining, and personalization.