PURE: Positive-unlabeled recommendation with generative adversarial network

Yao Zhou

Abstract:

Recommender systems are powerful tools for information filtering
with the ever-growing amount of online data. Despite its success
and wide adoption in various web applications and personalized
products, many existing recommender systems still suffer from
multiple drawbacks such as large amount of unobserved feedback,
poor model convergence, etc. These drawbacks of existing work
are mainly due to the following two reasons: first, the widely used
negative sampling strategy, which treats the unlabeled entries as
negative samples, is invalid in real-world settings; second, all training samples are retrieved from the discrete observations, and the
underlying true distribution of the users and items is not learned.
In this paper, we address these issues by developing a novel
framework named PURE, which trains an unbiased positive-unlabeled
discriminator to distinguish the true relevant user-item pairs against
the ones that are non-relevant, and a generator that learns the underlying user-item continuous distribution. For a comprehensive
comparison, we considered 14 popular baselines from 5 different categories of recommendation approaches. Extensive experiments on
two public real-world data sets demonstrate that PURE achieves the
best performance in terms of 8 ranking based evaluation metrics