Decision Tree Learning from Incomplete QoS to Bootstrap Service Recommendation

Collaborative Filtering (CF) has been increasingly employed as an effective vehicle for providing personalized service recommendations in service computing. CF exploits historical user-service interaction information to predict the preference of service users. A key challenge faced by CF is to handle new users with no previous interaction information. We present a novel strategy that integrates Matrix Factorization (MF) with decision tree learning to bootstrap service recommendation systems. The proposed strategy first employs MF to partition existing users into a set of user groups. In practice, only a small amount of user-service interaction information is observed. The MF based user partitioning scheme also provides a way to estimate the missing interaction information based on the group structure. The tree learning algorithm then leverages these estimated information and exploits user groups as class labels to learn a decision tree. Few highly discriminative services are identified as tree nodes to adaptively query a new user based on the interaction results with the prior services in the tree. Through a short and intuitive bootstrapping process, the new user is classified into one of the user groups, via which the user´s preference is predicted. We conduct a set of experiments on real-world service data to demonstrate the effectiveness of the proposed bootstrapping strategy.