Discovering What You're Known For Conference Paper uri icon


  • © 2016 ACM. Discovering what people are known for is valuable to many important applications such as recommender systems. Un- like an individual's personal interests, what a user is known for is reected by the views of others, and is often not eas- ily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user's content, it naturally models and inte- grates additional contextual factors, concretely, user's geo- spatial footprints and social inuence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in pre- cision and 20.9% in recall on average, and that it can capture the implicit relationships between a user's known-for profile and her content, geo-spatial and social inuence.

author list (cited authors)

  • Lu, H., Caverlee, J., & Niu, W.

editor list (cited editors)

  • Sen, S., Geyer, W., Freyne, J., & Castells, P.

publication date

  • January 1, 2016 11:11 AM


  • ACM  Publisher