Discovering What You're Known For: A Contextual Poisson Factorization Approach
Conference Paper
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
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.
name of conference
Proceedings of the 10th ACM Conference on Recommender Systems