The Web provides access to a wealth of information to a huge diverse user population on a global scale. One successful mechanism in dealing with this diversity of users is to personalize Web sites, services, and system content and customize for a specific user. Since this process currently occurs separately within each system, there are several drawbacks over an integrated approach. Cross system personalization (CSP) allows for sharing information across different information systems in a user-centric way and can overcome the aforementioned problems. Information about users, which is originally scattered across multiple systems, is combined to obtain maximum leverage and reuse. This book explains a principled approach towards achieving cross system personalization. We describe two approaches for CSP: semantic and learning-based, with a stronger emphasis on the learning approach. We also investigate the privacy and scalability aspects of CSP and provide solutions to these problems. Finally, we also explore in detail the aspect of robustness in recommender systems.

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