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Machine Learning and Data Mining Methods for Recommender Systems and Chemical Informatics

Research Area:  Machine Learning


This thesis focuses on machine learning and data mining methods for problems arising primarily in recommender systems and chemical informatics. Although these two areas represent dramatically different application domains, many of the underlying problems have common characteristics, which allows the transfer of ideas and methods between them.The first part of this thesis focuses on recommender systems. Recommender systems represent a set of computational methods that produce recommendations of interesting entities (e.g.,products) from a large collection of such entities by retrieving/filtering/learning information from their own properties (e.g., product attributes) and/or the interactions with other parties(e.g., user-product ratings).We have addressed the two core tasks for recommender systems, that is,top-N recommendation and rating prediction.We have developed 1).a novel sparse linear method for top N recommendation,which utilizes regularized linear regression with sparsity constraints to model user-item purchase patterns; 2).a set of novel sparse linear methods with side information for top-N recommendation,which use side information to regularize sparse linear models or use side information to model user-item purchase behaviors; and 3).a multi-task learning method for rating prediction, which uses multi-task learning methodologies to model user communities and predict personalized ratings.The second part of this thesis is dedicated to chemical informatics, which is an inter disciplinary research area where computational and information technologies are developed to aid the investigation of chemical problems.

Name of the Researcher:  Xia Ning

Name of the Supervisor(s):  Dr. George Karypis

Year of Completion:  2012


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