  Nowadays, users open multiple accounts on social media platforms and
e-commerce sites, expressing their personal preferences on different domains.
However, users' behaviors change across domains, depending on the content that
users interact with, such as movies, music, clothing and retail products. In
this paper, we propose an adaptive deep learning strategy for cross-domain
recommendation, referred to as ADC. We design a neural architecture and
formulate a cross-domain loss function, to compute the non-linearity in user
preferences across domains and transfer the knowledge of users' multiple
behaviors, accordingly. In addition, we introduce an efficient algorithm for
cross-domain loss balancing which directly tunes gradient magnitudes and adapts
the learning rates based on the domains' complexities/scales when training the
model via backpropagation. In doing so, ADC controls and adjusts the
contribution of each domain when optimizing the model parameters. Our
experiments on six publicly available cross-domain recommendation tasks
demonstrate the effectiveness of the proposed ADC model over other
state-of-the-art methods. Furthermore, we study the effect of the proposed
adaptive deep learning strategy and show that ADC can well balance the impact
of the domains with different complexities.
