Billions of people spend their virtual life time on hundreds of social networking sites for different social needs. Each social footprint of a person in a particular social networking site reflects some special aspects of himself. To adequately investigate a user’s preference for applications such as recommendation and executive search, we need to connect up all these aspects to generate a comprehensive profile of the identity. Profile linkage provides an effective solution to identify the same identity’s profiles from different social networks.
With various types of resources, comparing profiles may require plenty of expensive and time-consuming features such as avatars. To boost the online social network profile linkage solution, we propose a cost-sensitive approach that only acquires these expensive and time-consuming features when needed. By evaluating on the real-world datasets from Twitter and LinkedIn, our approach performs at over 85% F1-measure and has the ability to prune over 80% of the unnecessary feature acquisitions, at a marginal cost of 10% performance loss.