  Deep neural networks (DNN) have achieved great success in the recommender
systems (RS) domain. However, to achieve remarkable performance, DNN-based
recommender models often require numerous parameters, which inevitably bring
redundant neurons and weights, a phenomenon referred to as
over-parameterization. In this paper, we plan to exploit such redundancy
phenomena to improve the performance of RS. Specifically, we propose PCRec, a
top-N item \underline{rec}ommendation framework that leverages collaborative
training of two DNN-based recommender models with the same network structure,
termed \underline{p}eer \underline{c}ollaboration. PCRec can reactivate and
strengthen the unimportant (redundant) weights during training, which achieves
higher prediction accuracy but maintains its original inference efficiency. To
realize this, we first introduce two criteria to identify the importance of
weights of a given recommender model. Then, we rejuvenate the unimportant
weights by transplanting outside information (i.e., weights) from its peer
network. After such an operation and retraining, the original recommender model
is endowed with more representation capacity by possessing more functional
model parameters. To show its generality, we instantiate PCRec by using three
well-known recommender models. We conduct extensive experiments on three
real-world datasets, and show that PCRec yields significantly better
recommendations than its counterpart with the same model (parameter) size.
