  Re-ranking draws increased attention on both academics and industries, which
rearranges the ranking list by modeling the mutual influence among items to
better meet users' demands. Many existing re-ranking methods directly take the
initial ranking list as input, and generate the optimal permutation through a
well-designed context-wise model, which brings the evaluation-before-reranking
problem. Meanwhile, evaluating all candidate permutations brings unacceptable
computational costs in practice. Thus, to better balance efficiency and
effectiveness, online systems usually use a two-stage architecture which uses
some heuristic methods such as beam-search to generate a suitable amount of
candidate permutations firstly, which are then fed into the evaluation model to
get the optimal permutation. However, existing methods in both stages can be
improved through the following aspects. As for generation stage, heuristic
methods only use point-wise prediction scores and lack an effective judgment.
As for evaluation stage, most existing context-wise evaluation models only
consider the item context and lack more fine-grained feature context modeling.
This paper presents a novel end-to-end re-ranking framework named PIER to
tackle the above challenges which still follows the two-stage architecture and
contains two mainly modules named FPSM and OCPM. We apply SimHash in FPSM to
select top-K candidates from the full permutation based on user's
permutation-level interest in an efficient way. Then we design a novel
omnidirectional attention mechanism in OCPM to capture the context information
in the permutation. Finally, we jointly train these two modules end-to-end by
introducing a comparative learning loss. Offline experiment results demonstrate
that PIER outperforms baseline models on both public and industrial datasets,
and we have successfully deployed PIER on Meituan food delivery platform.
