  As the final stage of the multi-stage recommender system (MRS), re-ranking
directly affects user experience and satisfaction by rearranging the input
ranking lists, and thereby plays a critical role in MRS. With the advances in
deep learning, neural re-ranking has become a trending topic and been widely
applied in industrial applications. This review aims at integrating re-ranking
algorithms into a broader picture, and paving ways for more comprehensive
solutions for future research. For this purpose, we first present a taxonomy of
current methods on neural re-ranking. Then we give a description of these
methods along with the historic development according to their objectives. The
network structure, personalization, and complexity are also discussed and
compared. Next, we provide benchmarks of the major neural re-ranking models and
quantitatively analyze their re-ranking performance. Finally, the review
concludes with a discussion on future prospects of this field. A list of papers
discussed in this review, the benchmark datasets, our re-ranking library
LibRerank, and detailed parameter settings are publicly available at
https://github.com/LibRerank-Community/LibRerank.
