  Image based social networks are among the most popular social networking
services in recent years. With tremendous images uploaded everyday,
understanding users' preferences on user-generated images and making
recommendations have become an urgent need. In fact, many hybrid models have
been proposed to fuse various kinds of side information~(e.g., image visual
representation, social network) and user-item historical behavior for enhancing
recommendation performance. However, due to the unique characteristics of the
user generated images in social image platforms, the previous studies failed to
capture the complex aspects that influence users' preferences in a unified
framework. Moreover, most of these hybrid models relied on predefined weights
in combining different kinds of information, which usually resulted in
sub-optimal recommendation performance. To this end, in this paper, we develop
a hierarchical attention model for social contextual image recommendation. In
addition to basic latent user interest modeling in the popular matrix
factorization based recommendation, we identify three key aspects (i.e., upload
history, social influence, and owner admiration) that affect each user's latent
preferences, where each aspect summarizes a contextual factor from the complex
relationships between users and images. After that, we design a hierarchical
attention network that naturally mirrors the hierarchical relationship
(elements in each aspects level, and the aspect level) of users' latent
interests with the identified key aspects. Specifically, by taking embeddings
from state-of-the-art deep learning models that are tailored for each kind of
data, the hierarchical attention network could learn to attend differently to
more or less content. Finally, extensive experimental results on real-world
datasets clearly show the superiority of our proposed model.
