Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Modal Adversarial Autoencoders for Recommendations of Citations
  and Subject Labels   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.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: eess
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio