Same places, same things, same people?: mining user similarity on social media
Proceedings of the 2010 ACM conference on Computer supported cooperative work
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Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Information Retrieval
Probabilistic latent class models for predicting student performance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Identifying 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.