User Adaptive Recommendation Model by Using User Clustering based on Decision Tree

With the rapid growth of information and communication technology, many researchers are studying on development of user adaptive recommendation systems for user centric services. Most of the recommendation systems are being studied on using content-based and collaborative recommendation methods. However, these systems have the problems such as taking too much time for analyzing characteristics of new users or new services when they come into the system and generating too simple recommendation results due to the properties known as overspecialization and sparsity. In this paper, we propose an agent based recommendation model that can reduce analysis time when new users or new services appear in the system and recommend more user centric services. Proposed model clusters existing users by using decision tree and analyzes new incoming users by traversing the decision tree, which has already been constructed into the structure that reduces the analysis time. To prove the effectiveness of the proposed model, we implement user clustering and service recommendation scheme using decision tree, and evaluate its performance with some experimentations.