Abstract
This paper proposes Personalized Diversitypromoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant
recommendations. Specifically, for each user, a
generator recommends a set of diverse and relevant
items by sequentially sampling from a personalized
Determinantal Point Process (DPP) kernel matrix.
This kernel matrix is constructed by two learnable
components: the general co-occurrence of diverse
items and the user’s personal preference to items.
To learn the first component, we propose a novel
pairwise learning paradigm using training pairs,
and each training pair consists of a set of diverse
items and a set of similar items randomly sampled
from the observed data of all users. The second
component is learnt through adversarial training
against a discriminator which strives to distinguish
between recommended items and the ground-truth
sets randomly sampled from the observed data of
the target user. Experimental results show that
PD-GAN is superior to generate recommendations
that are both diverse and relevant