International Journal of Web Services Research
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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.