Cross System Personalization
Enabling personalization across multiple systems
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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 s...
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.
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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