  In one-class recommendation systems, the goal is to learn a model from a
small set of interacted users and items and then identify the
positively-related user-item pairs among a large number of pairs with unknown
interactions. Most previous loss functions rely on dissimilar pairs of users
and items, which are selected from the ones with unknown interactions, to
obtain better prediction performance. This strategy introduces several
challenges such as increasing training time and hurting the performance by
picking "similar pairs with the unknown interactions" as dissimilar pairs. In
this paper, the goal is to only use the similar set to train the models. We
point out three trivial solutions that the models converge to when they are
trained only on similar pairs: collapsed, partially collapsed, and shrinking
solutions. We propose two terms that can be added to the objective functions in
the literature to avoid these solutions. The first one is a hinge pairwise
distance loss that avoids the shrinking and collapsed solutions by keeping the
average pairwise distance of all the representations greater than a margin. The
second one is an orthogonality term that minimizes the correlation between the
dimensions of the representations and avoids the partially collapsed solution.
We conduct experiments on a variety of tasks on public and real-world datasets.
The results show that our approach using only similar pairs outperforms
state-of-the-art methods using similar pairs and a large number of dissimilar
pairs.
