Mediation of User Models
for Enhanced Personalization in Recommender Systems
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Provision of personalized recommendations to usersrequires accurate modeling of their interests andneeds. This book presents a general framework andspecific methodologies for enhancing the accuracy ofuser modeling in recommender systems by importing andintegrating data collected by other recommendersystems. Such a process is defined as the mediationof user models and user modeling data. This bookdiscusses the details of the generic user modelingmediation framework, provides a user modeling datarepresentation model, demonstrates the compatibilityof the model with existing recommendation techniq...
Provision of personalized recommendations to users
requires accurate modeling of their interests and
needs. This book presents a general framework and
specific methodologies for enhancing the accuracy of
user modeling in recommender systems by importing and
integrating data collected by other recommender
systems. Such a process is defined as the mediation
of user models and user modeling data. This book
discusses the details of the generic user modeling
mediation framework, provides a user modeling data
representation model, demonstrates the compatibility
of the model with existing recommendation techniques,
and discusses the general steps of the mediation.
Then, the mediation framework is applied and
illustrated with two practical mediation scenarios:
cross-technique mediation and cross-domain
mediation. Empirical evaluation of these scenarios
shows that the mediation of user modeling data is
practical and beneficial, as it allows upgrading the
quality of the recommendations provided to the users.
requires accurate modeling of their interests and
needs. This book presents a general framework and
specific methodologies for enhancing the accuracy of
user modeling in recommender systems by importing and
integrating data collected by other recommender
systems. Such a process is defined as the mediation
of user models and user modeling data. This book
discusses the details of the generic user modeling
mediation framework, provides a user modeling data
representation model, demonstrates the compatibility
of the model with existing recommendation techniques,
and discusses the general steps of the mediation.
Then, the mediation framework is applied and
illustrated with two practical mediation scenarios:
cross-technique mediation and cross-domain
mediation. Empirical evaluation of these scenarios
shows that the mediation of user modeling data is
practical and beneficial, as it allows upgrading the
quality of the recommendations provided to the users.
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