  With the prosperity of business intelligence, recommender systems have
evolved into a new stage that we not only care about what to recommend, but why
it is recommended. Explainability of recommendations thus emerges as a focal
point of research and becomes extremely desired in e-commerce. Existent studies
along this line often exploit item attributes and correlations from different
perspectives, but they yet lack an effective way to combine both types of
information for deep learning of personalized interests. In light of this, we
propose a novel graph structure, \emph{attribute network}, based on both items'
co-purchase network and important attributes. A novel neural model called
\emph{eRAN} is then proposed to generate recommendations from attribute
networks with explainability and cold-start capability. Specifically, eRAN
first maps items connected in attribute networks to low-dimensional embedding
vectors through a deep autoencoder, and then an attention mechanism is applied
to model the attractions of attributes to users, from which personalized item
representation can be derived. Moreover, a pairwise ranking loss is constructed
into eRAN to improve recommendations, with the assumption that item pairs
co-purchased by a user should be more similar than those non-paired with
negative sampling in personalized view. Experiments on real-world datasets
demonstrate the effectiveness of our method compared with some state-of-the-art
competitors. In particular, eRAN shows its unique abilities in recommending
cold-start items with higher accuracy, as well as in understanding user
preferences underlying complicated co-purchasing behaviors.
