  Reranking is attracting incremental attention in the recommender systems,
which rearranges the input ranking list into the final rank-ing list to better
meet user demands. Most existing methods greedily rerank candidates through the
rating scores from point-wise or list-wise models. Despite effectiveness,
neglecting the mutual influence between each item and its contexts in the final
ranking list often makes the greedy strategy based reranking methods
sub-optimal. In this work, we propose a new context-wise reranking framework
named Generative Rerank Network (GRN). Specifically, we first design the
evaluator, which applies Bi-LSTM and self-attention mechanism to model the
contextual information in the labeled final ranking list and predict the
interaction probability of each item more precisely. Afterwards, we elaborate
on the generator, equipped with GRU, attention mechanism and pointer network to
select the item from the input ranking list step by step. Finally, we apply
cross-entropy loss to train the evaluator and, subsequently, policy gradient to
optimize the generator under the guidance of the evaluator. Empirical results
show that GRN consistently and significantly outperforms state-of-the-art
point-wise and list-wise methods. Moreover, GRN has achieved a performance
improvement of 5.2% on PV and 6.1% on IPV metric after the successful
deployment in one popular recommendation scenario of Taobao application.
