A Cluster-Aware Transfer Learning for Bayesian Optimization of Personalized Preference Models

Obtaining personalized models of the crowd is an important issue in various applications, such as preference acquisition and user interaction customization. However, the crowd setting, in which we assume we have little knowledge about the person, brings the cold start problem, which may cause avoidable unpreferable interactions with the people. This paper proposes a cluster-aware transfer learning method for the Bayesian optimization of personalized models. The proposed method, called Cluster-aware Bayesian Optimization, is designed based on a known feature: user preferences are not completely independent but can be divided into clusters. It exploits the clustering information to efficiently find the preference of the crowds while avoiding unpreferable interactions. The results of our extensive experiments with different data sets show that the method is efficient for finding the most preferable items and effective in reducing the number of unpreferable interactions.