  We present multi-modal adversarial autoencoders for recommendation and
evaluate them on two different tasks: citation recommendation and subject label
recommendation. We analyze the effects of adversarial regularization, sparsity,
and different input modalities. By conducting 408 experiments, we show that
adversarial regularization consistently improves the performance of
autoencoders for recommendation. We demonstrate, however, that the two tasks
differ in the semantics of item co-occurrence in the sense that item
co-occurrence resembles relatedness in case of citations, yet implies diversity
in case of subject labels. Our results reveal that supplying the partial item
set as input is only helpful, when item co-occurrence resembles relatedness.
When facing a new recommendation task it is therefore crucial to consider the
semantics of item co-occurrence for the choice of an appropriate model.
