Today, personalization in digital libraries and other information systems occurs separately within each system that one interacts with. However, there are several potential improvements w.r.t. such isolated approaches. Investments of users in personalizing a system, either through explicit provision of information, or through long and regular use are not transferable to other systems. Moreover, users have little or no control over the information that defines their profile, since user profiles are deeply buried in personalization engines. Cross-system personalization, i.e. personalization that shares personalization information across different systems in a user-centric way, overcomes the aforementioned problems. Information about users, which is originally scattered across multiple systems, is combined to obtain maximum leverage. The key idea is that when a large number of users cross over from one system to another, carrying their user profiles with them, a mapping between the user profiles of the two systems can be discovered. In this work, we discuss the use of manifold learning for the purpose of computing recomendations for a new user crossing over from one system to another.
Subjects: 12. Machine Learning and Discovery; 1.10 Information Retrieval