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

Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels