Designing and evaluating intelligent context-aware recommender systems: methodologies and applications
Date Issued
December 2017
Author(s)
Advisor
Abstract
This research introduces new concepts and methodologies for Recommender Systems
aiming to enhance the user experience and at the same time to improve the system’s
accuracy by dealing with the challenges of RS. The thesis and the corresponding research
is structured in three main parts. The first part of this thesis concentrates more on the
development of new Multi-criteria RS to improve the accuracy and performance of RS.
Our study examines solutions on how to deal with data sparsity, scalability issues and the
cold-start problem by utilizing various techniques. The second part deals with the
classification prediction problem. We propose a new methodology for developing hybrid
models to improve the accuracy of classification models and thus provide better
recommendations. The final part introduces a Recurrent Latent Variable framework based
on a variational Recurrent Neural Network that deals with data sparsity and uncertainty
met on session-based recommendations and sequence-based data. Experimentation was
performed in all three parts mentioned and the results demonstrated the validity of the
proposed methodologies when compared with state-of-the-art methods.
aiming to enhance the user experience and at the same time to improve the system’s
accuracy by dealing with the challenges of RS. The thesis and the corresponding research
is structured in three main parts. The first part of this thesis concentrates more on the
development of new Multi-criteria RS to improve the accuracy and performance of RS.
Our study examines solutions on how to deal with data sparsity, scalability issues and the
cold-start problem by utilizing various techniques. The second part deals with the
classification prediction problem. We propose a new methodology for developing hybrid
models to improve the accuracy of classification models and thus provide better
recommendations. The final part introduces a Recurrent Latent Variable framework based
on a variational Recurrent Neural Network that deals with data sparsity and uncertainty
met on session-based recommendations and sequence-based data. Experimentation was
performed in all three parts mentioned and the results demonstrated the validity of the
proposed methodologies when compared with state-of-the-art methods.
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