  Personalized recommender systems are playing an increasingly important role
as more content and services become available and users struggle to identify
what might interest them. Although matrix factorization and deep learning based
methods have proved effective in user preference modeling, they violate the
triangle inequality and fail to capture fine-grained preference information. To
tackle this, we develop a distance-based recommendation model with several
novel aspects: (i) each user and item are parameterized by Gaussian
distributions to capture the learning uncertainties; (ii) an adaptive margin
generation scheme is proposed to generate the margins regarding different
training triplets; (iii) explicit user-user/item-item similarity modeling is
incorporated in the objective function. The Wasserstein distance is employed to
determine preferences because it obeys the triangle inequality and can measure
the distance between probabilistic distributions. Via a comparison using five
real-world datasets with state-of-the-art methods, the proposed model
outperforms the best existing models by 4-22% in terms of recall@K on Top-K
recommendation.
