Discovering What You're Known For: A Contextual Poisson Factorization Approach
Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual's personal interests, what a user is known for is reflected by the views of others, and is often not easily 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 integrates additional contextual factors, concretely, user's geo spatial footprints and social influence, 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 precision 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 influence.
RecSys