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Bayesian recommender systems : models and algorithms

Guo, Shengbo


This thesis is about how Bayesian methods can be applied to explicitly model and efficiently reason about uncertainty to make optimal recommendations. We are interested in three dimensions of recommender systems: (1) preference elicitation, (2) set-based recommendations, and (3) matchmaking. The first dimension concerns how one can minimize the elicitation efforts in learning a user's utility function to propose the maximal utility recommendation. The second dimension concerns set-based...[Show more]

CollectionsOpen Access Theses
Type: Thesis (PhD)


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