Contrastive Sequential Interaction Network Learning on Co-Evolving
  Riemannian Spaces
Li Sun Junda Ye Jiawei Zhang Yong Yang Mingsheng Liu Feiyang Wang Philip S. Yu
The sequential interaction network usually find itself in a variety of
applications, e.g., recommender system. Herein, inferring future interaction is
of fundamental importance, and previous efforts are mainly focused on the
dynamics in the classic zero-curvature Euclidean space. Despite the promising
results achieved by previous methods, a range of significant issues still
largely remains open: On the bipartite nature, is it appropriate to place user
and item nodes in one identical space regardless of their inherent difference?
On the network dynamics, instead of a fixed curvature space, will the
representation spaces evolve when new interactions arrive continuously? On the
learning paradigm, can we get rid of the label information costly to acquire?
To address the aforementioned issues, we propose a novel Contrastive model for
Sequential Interaction Network learning on Co-Evolving RiEmannian spaces,
CSINCERE. To the best of our knowledge, we are the first to introduce a couple
of co-evolving representation spaces, rather than a single or static space, and
propose a co-contrastive learning for the sequential interaction network. In
CSINCERE, we formulate a Cross-Space Aggregation for message-passing across
representation spaces of different Riemannian geometries, and design a Neural
Curvature Estimator based on Ricci curvatures for modeling the space evolvement
over time. Thereafter, we present a Reweighed Co-Contrast between the temporal
views of the sequential network, so that the couple of Riemannian spaces
interact with each other for the interaction prediction without labels.
Empirical results on 5 public datasets show the superiority of CSINCERE over
the state-of-the-art methods.