  The behavior of users in certain services could be a clue that can be used to
infer their preferences and may be used to make recommendations for other
services they have never used. However, the cross-domain relationships between
items and user consumption patterns are not simple, especially when there are
few or no common users and items across domains. To address this problem, we
propose a content-based cross-domain recommendation method for cold-start users
that does not require user- and item- overlap. We formulate recommendation as
extreme multi-class classification where labels (items) corresponding to the
users are predicted. With this formulation, the problem is reduced to a domain
adaptation setting, in which a classifier trained in the source domain is
adapted to the target domain. For this, we construct a neural network that
combines an architecture for domain adaptation, Domain Separation Network, with
a denoising autoencoder for item representation. We assess the performance of
our approach in experiments on a pair of data sets collected from movie and
news services of Yahoo! JAPAN and show that our approach outperforms several
baseline methods including a cross-domain collaborative filtering method.
