  User behavior data produced during interaction with massive items in the
significant data era are generally heterogeneous and sparse, leaving the
recommender system (RS) a large diversity of underlying patterns to excavate.
Deep neural network-based models have reached the state-of-the-art benchmark of
the RS owing to their fitting capabilities. However, prior works mainly focus
on designing an intricate architecture with fixed loss function and regulation.
These single-metric models provide limited performance when facing
heterogeneous and sparse user behavior data. Motivated by this finding, we
propose a multi-metric AutoRec (MMA) based on the representative AutoRec. The
idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on
loss function and regularization to form different variant models in different
metric spaces, and 2) aggregate these variant models. Thus, the proposed MMA
enjoys the multi-metric orientation from a set of dispersed metric spaces,
achieving a comprehensive representation of user data. Theoretical studies
proved that the proposed MMA could attain performance improvement. The
extensive experiment on five real-world datasets proves that MMA can outperform
seven other state-of-the-art models in predicting unobserved user behavior
data.
