  In recent years, there are numerous works been proposed to leverage the
techniques of deep learning to improve social-aware recommendation performance.
In most cases, it requires a larger number of data to train a robust deep
learning model, which contains a lot of parameters to fit training data.
However, both data of user ratings and social networks are facing critical
sparse problem, which makes it not easy to train a robust deep neural network
model. Towards this problem, we propose a novel Correlative Denoising
Autoencoder (CoDAE) method by taking correlations between users with multiple
roles into account to learn robust representations from sparse inputs of
ratings and social networks for recommendation. We develop the CoDAE model by
utilizing three separated autoencoders to learn user features with roles of
rater, truster and trustee, respectively. Especially, on account of that each
input unit of user vectors with roles of truster and trustee is corresponding
to a particular user, we propose to utilize shared parameters to learn common
information of the units that corresponding to same users. Moreover, we propose
a related regularization term to learn correlations between user features that
learnt by the three subnetworks of CoDAE model. We further conduct a series of
experiments to evaluate the proposed method on two public datasets for Top-N
recommendation task. The experimental results demonstrate that the proposed
model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP
and NDCG.
