  Tag recommendation relies on either a ranking function for top-$k$ tags or an
autoregressive generation method. However, the previous methods neglect one of
two seemingly conflicting yet desirable characteristics of a tag set:
orderlessness and inter-dependency. While the ranking approach fails to address
the inter-dependency among tags when they are ranked, the autoregressive
approach fails to take orderlessness into account because it is designed to
utilize sequential relations among tokens. We propose a sequence-oblivious
generation method for tag recommendation, in which the next tag to be generated
is independent of the order of the generated tags and the order of the ground
truth tags occurring in training data. Empirical results on two different
domains, Instagram and Stack Overflow, show that our method is significantly
superior to the previous approaches.
