Identifying Similar People in Professional Social Networks with Discriminative Probabilistic Models
dentifying similar professionals is an important task for many core services in professional social networks. Information about users can be obtained from heterogeneous information sources, and different sources provide different insights on user similarity.
This paper proposes a discriminative probabilistic model that identifies latent content and graph classes for people with similar profile content and social graph similarity patterns, and learns a specialized similarity model for each latent class. To the best of our knowledge, this is the first work on identifying similar professionals in professional social networks, and the first work that identifies latent classes to learn a separate similarity model for each latent class. Experiments on a real-world dataset demonstrate the effectiveness of the proposed discriminative learning model.
discriminative learning, information search and retrieval, similar people, social networks, theory
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