  Reranking, as the final stage of multi-stage recommender systems, refines the
initial lists to maximize the total utility. With the development of multimedia
and user interface design, the recommendation page has evolved to a multi-list
style. Separately employing traditional list-level reranking methods for
different lists overlooks the inter-list interactions and the effect of
different page formats, thus yielding suboptimal reranking performance.
Moreover, simply applying a shared network for all the lists fails to capture
the commonalities and distinctions in user behaviors on different lists. To
this end, we propose to draw a bird's-eye view of \textbf{page-level reranking}
and design a novel Page-level Attentional Reranking (PAR) model. We introduce a
hierarchical dual-side attention module to extract personalized intra- and
inter-list interactions. A spatial-scaled attention network is devised to
integrate the spatial relationship into pairwise item influences, which
explicitly models the page format. The multi-gated mixture-of-experts module is
further applied to capture the commonalities and differences of user behaviors
between different lists. Extensive experiments on a public dataset and a
proprietary dataset show that PAR significantly outperforms existing baseline
models.
