Who to follow and why: link prediction with explanations

@article{Barbieri2014WhoTF,
  title={Who to follow and why: link prediction with explanations},
  author={Nicola Barbieri and F. Bonchi and G. Manco},
  journal={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},
  year={2014}
}
  • Nicola Barbieri, F. Bonchi, G. Manco
  • Published 2014
  • Computer Science
  • Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty. In this paper we study link prediction with explanations for user recommendation in social networks. For this problem we propose WTFW ("Who to Follow and Why"), a stochastic topic model for link prediction over directed and nodes-attributed graphs. Our model not only predicts links, but for each predicted link it decides… Expand
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References

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network