  With the growing importance of personalized recommendation, numerous
recommendation models have been proposed recently. Among them, Matrix
Factorization (MF) based models are the most widely used in the recommendation
field due to their high performance. However, MF based models suffer from cold
start problems where user-item interactions are sparse. To deal with this
problem, content based recommendation models which use the auxiliary attributes
of users and items have been proposed. Since these models use auxiliary
attributes, they are effective in cold start settings. However, most of the
proposed models are either unable to capture complex feature interactions or
not properly designed to combine user-item feedback information with content
information. In this paper, we propose Self-Attentive Integration Network
(SAIN) which is a model that effectively combines user-item feedback
information and auxiliary information for recommendation task. In SAIN, a
self-attention mechanism is used in the feature-level interaction layer to
effectively consider interactions between multiple features, while the
information integration layer adaptively combines content and feedback
information. The experimental results on two public datasets show that our
model outperforms the state-of-the-art models by 2.13%
