Model Adaptation with Bayesian Hierarchical Modeling for Context-Aware Recommendation

Model adaptation is a process of modifying a model trained with a large amount of training data from the source domain to adapt a speci c similar target domain by using a small amount of adaptation data regarding the target domain. Bayesian hierarchical modeling is well known as a general tool for model adaptation and multi-task learning, and widely used in various areas such as marketing, ecology, medicine, education, and so on in order to model the heterogeneity in the phenomena. In this work, we propose to apply the Bayesian hierarchical modeling to the problem of preference modeling, where a model trained with a large amount of supposed context data is adapted to the real context by using additional small amount of real context data. The e ectiveness of the proposed method is evaluated by experiments using context-aware food preference data.