  Implicit feedback is widely explored by modern recommender systems. Since the
feedback is often sparse and imbalanced, it poses great challenges to the
learning of complex interactions among users and items. Metric learning has
been proposed to capture user-item interactions from implicit feedback, but
existing methods only represent users and items in a single metric space,
ignoring the fact that users can have multiple preferences and items can have
multiple properties, which leads to potential conflicts limiting their
performance in recommendation. To capture the multiple facets of user
preferences and item properties while resolving their potential conflicts, we
propose the novel framework of Multi-fAcet Recommender networks with Spherical
optimization (MARS). By designing a cross-facet similarity measurement, we
project users and items into multiple metric spaces for fine-grained
representation learning, and compare them only in the proper spaces.
Furthermore, we devise a spherical optimization strategy to enhance the
effectiveness and robustness of the multi-facet recommendation framework.
Extensive experiments on six real-world benchmark datasets show drastic
performance gains brought by MARS, which constantly achieves up to 40\%
improvements over the state-of-the-art baselines regarding both HR and nDCG
metrics.
