Abstract
In recent years, deep neural networks have yielded state-of-the-art
performance on several tasks. Although some recent works have focused on
combining deep learning with recommendation, we highlight three issues of
existing works. First, most works perform deep content feature learning and
resort to matrix factorization, which cannot effectively model the highly
complex user-item interaction function. Second, due to the difficulty on
training deep neural networks, existing models utilize a shallow architecture,
and thus limit the expressive potential of deep learning. Third, neural network
models are easy to overfit on the implicit setting, because negative
interactions are not taken into account. To tackle these issues, we present a
generic recommender framework called Neural Collaborative Autoencoder (NCAE) to
perform collaborative filtering, which works well for both explicit feedback
and implicit feedback. NCAE can effectively capture the relationship between
interactions via a non-linear matrix factorization process. To optimize the
deep architecture of NCAE, we develop a three-stage pre-training mechanism that
combines supervised and unsupervised feature learning. Moreover, to prevent
overfitting on the implicit setting, we propose an error reweighting module and
a sparsity-aware data-augmentation strategy. Extensive experiments on three
real-world datasets demonstrate that NCAE can significantly advance the
state-of-the-art.
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