Abstract. The context-awareness has become one of the core technologies and the indispensable function for application services in ubiquitous computing environment. The task of using context data for inferring a user's situation is referred to as context reasoning. In this research, we incorporated the capability of context reasoning in a music recommendation system. Our proposed system contains such modules as Intention Module, Mood Module and Recommendation Module. The Intention Module performs context reasoning that infers whether a user wants to listen to music or not by using the environmental context data. The Mood Module determines the genre of the music suitable to the user's context. Finally, the Recommendation Module recommends the music to the user. Context reasoning is implemented using case-based reasoning.

Abstract. In order to improve the performance of a data mining model, many researchers have employed a hybrid model approach in solving a problem. There are two types of approach to build a hybrid model, i.e., the whole data approach and the segmented data approach. In this research, we present a new structure of the latter type of hybrid model, which we shall call SePI. In the SePI, input data is segmented using the performance information of the models tried in the training phase. We applied the SePI to a real customer churn problem of a Korean company that provides streaming digital music services through Internet. The result shows that the SePI outperformed any model that employed only one data mining technique such as artificial neural network, decision tree and logistic regression.