  This paper explores the use of hyperbolic geometry and deep learning
techniques for recommendation. We present Hyperbolic Neural Collaborative
Recommender (HNCR), a deep hyperbolic representation learning method that
exploits mutual semantic relations among users/items for collaborative
filtering (CF) tasks. HNCR contains two major phases: neighbor construction and
recommendation framework. The first phase introduces a neighbor construction
strategy to construct a semantic neighbor set for each user and item according
to the user-item historical interaction. In the second phase, we develop a deep
framework based on hyperbolic geometry to integrate constructed neighbor sets
into recommendation. Via a series of extensive experiments, we show that HNCR
outperforms its Euclidean counterpart and state-of-the-art baselines.
