  A major drawback of cross-network recommender solutions is that they can only
be applied to users that are overlapped across networks. Thus, the
non-overlapped users, which form the majority of users are ignored. As a
solution, we propose CnGAN, a novel multi-task learning based,
encoder-GAN-recommender architecture. The proposed model synthetically
generates source network user preferences for non-overlapped users by learning
the mapping from target to source network preference manifolds. The resultant
user preferences are used in a Siamese network based neural recommender
architecture. Furthermore, we propose a novel user based pairwise loss function
for recommendations using implicit interactions to better guide the generation
process in the multi-task learning environment.We illustrate our solution by
generating user preferences on the Twitter source network for recommendations
on the YouTube target network. Extensive experiments show that the generated
preferences can be used to improve recommendations for non-overlapped users.
The resultant recommendations achieve superior performance compared to the
state-of-the-art cross-network recommender solutions in terms of accuracy,
novelty and diversity.
