Cross System Personalization

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.

Autorentext

Bhaskar Mehta is a Computer Scientist who has focussed on User centric systems in his Research. He graduated in 2002 with a B.Tech and M.Tech in CS from IIT Delhi, and then worked for the Fraunhofer Institute in Darmstadt, Germany. He defended his PhD Thesis in 2008, and has worked with Google Inc since 2008.

Klappentext

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 sharinginformation across different information systems in a user-centric way and can overcome theaforementioned problems. Information about users, which is originally scattered across multiplesystems, 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 emphasison the learning approach. We also investigate the privacy and scalability aspects of CSP andprovide solutions to these problems. Finally, we also explore in detail the aspect of robustness in recommender systems.