Learning-to-rank is a core technique in the top-N recommendation task, where
an ideal ranker would be a mapping from an item set to an arrangement (a.k.a.
permutation). Most existing solutions fall in the paradigm of probabilistic
ranking principle (PRP), i.e., first score each item in the candidate set and
then perform a sort operation to generate the top ranking list. However, these
approaches neglect the contextual dependence among candidate items during
individual scoring, and the sort operation is non-differentiable. To bypass the
above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework
directly generates the permutations of the candidate items without the need for
individually scoring and sort operations; and is end-to-end differentiable. As
a result, STARank can operate when only the ground-truth permutations are
accessible without requiring access to the ground-truth relevance scores for
items. For this purpose, STARank first reads the candidate items in the context
of the user browsing history, whose representations are fed into a
Plackett-Luce module to arrange the given items into a list. To effectively
utilize the given ground-truth permutations for supervising STARank, we
leverage the internal consistency property of Plackett-Luce models to derive a
computationally efficient list-wise loss. Experimental comparisons against 9
the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3
top-N real-world recommendation datasets demonstrate the superiority of STARank
in terms of conventional ranking metrics. Notice that these ranking metrics do
not consider the effects of the contextual dependence among the items in the
list, we design a new family of simulation-based ranking metrics, where
existing metrics can be regarded as special cases. STARank can consistently
achieve better performance in terms of PBM and UBM simulation-based metrics.Comment: CIKM 202