  Proper initialization is crucial to the optimization and the generalization
of neural networks. However, most existing neural recommendation systems
initialize the user and item embeddings randomly. In this work, we propose a
new initialization scheme for user and item embeddings called Laplacian
Eigenmaps with Popularity-based Regularization for Isolated Data (LEPORID).
LEPORID endows the embeddings with information regarding multi-scale
neighborhood structures on the data manifold and performs adaptive
regularization to compensate for high embedding variance on the tail of the
data distribution. Exploiting matrix sparsity, LEPORID embeddings can be
computed efficiently. We evaluate LEPORID in a wide range of neural
recommendation models. In contrast to the recent surprising finding that the
simple K-nearest-neighbor (KNN) method often outperforms neural recommendation
systems, we show that existing neural systems initialized with LEPORID often
perform on par or better than KNN. To maximize the effects of the
initialization, we propose the Dual-Loss Residual Recommendation (DLR2)
network, which, when initialized with LEPORID, substantially outperforms both
traditional and state-of-the-art neural recommender systems.
