  When purchasing appearance-first products, e.g., clothes, product appearance
aesthetics plays an important role in the decision process. Moreover, user's
aesthetic preference, which can be regarded as a personality trait and a basic
requirement, is domain independent and could be used as a bridge between
domains for knowledge transfer. However, existing work has rarely considered
the aesthetic information in product photos for cross-domain recommendation. To
this end, in this paper, we propose a new deep Aesthetic preference
Cross-Domain Network (ACDN), in which parameters characterizing personal
aesthetic preferences are shared across networks to transfer knowledge between
domains. Specifically, we first leverage an aesthetic network to extract
relevant features. Then, we integrate the aesthetic features into a
cross-domain network to transfer users' domain independent aesthetic
preferences. Moreover, network cross-connections are introduced to enable dual
knowledge transfer across domains. Finally, the experimental results on
real-world data show that our proposed ACDN outperforms other benchmark methods
in terms of recommendation accuracy. The results also show that users'
aesthetic preferences are effective in alleviating the data sparsity issue on
the cross-domain recommendation.
