  Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate a
user's preference as a latent vector by encoding their historical interactions.
However, users often show diverse interests, which significantly increases the
learning difficulty. In order to capture multifaceted user preferences,
existing recommender systems either increase the encoding complexity or extend
the latent representation dimension. Unfortunately, these changes inevitably
lead to increased training difficulty and exacerbate scalability issues. In
this paper, we propose a novel and efficient CF framework called Attentive
Multi-modal AutoRec (AMA) that explicitly tracks multiple facets of user
preferences. Specifically, we extend the Autoencoding-based recommender AutoRec
to learn user preferences with multi-modal latent representations, where each
mode captures one facet of a user's preferences. By leveraging the attention
mechanism, each observed interaction can have different contributions to the
preference facets. Through extensive experiments on three real-world datasets,
we show that AMA is competitive with state-of-the-art models under the OC-CF
setting. Also, we demonstrate how the proposed model improves interpretability
by providing explanations using the attention mechanism.
