  The rapid growth of Internet services and mobile devices provides an
excellent opportunity to satisfy the strong demand for the personalized item or
product recommendation. However, with the tremendous increase of users and
items, personalized recommender systems still face several challenging
problems: (1) the hardness of exploiting sparse implicit feedback; (2) the
difficulty of combining heterogeneous data. To cope with these challenges, we
propose a gated attentive-autoencoder (GATE) model, which is capable of
learning fused hidden representations of items' contents and binary ratings,
through a neural gating structure. Based on the fused representations, our
model exploits neighboring relations between items to help infer users'
preferences. In particular, a word-level and a neighbor-level attention module
are integrated with the autoencoder. The word-level attention learns the item
hidden representations from items' word sequences, while favoring informative
words by assigning larger attention weights. The neighbor-level attention
learns the hidden representation of an item's neighborhood by considering its
neighbors in a weighted manner. We extensively evaluate our model with several
state-of-the-art methods and different validation metrics on four real-world
datasets. The experimental results not only demonstrate the effectiveness of
our model on top-N recommendation but also provide interpretable results
attributed to the attention modules.
