Neural Re-ranking in Multi-stage Recommender Systems: A Review
It addresses the need for a comprehensive overview of neural re-ranking techniques to improve user experience in recommender systems, but is incremental as it reviews existing methods rather than proposing new ones.
This review paper synthesizes neural re-ranking methods in multi-stage recommender systems, categorizing them by objectives and comparing their network structures, personalization, and complexity, while providing benchmarks and a public library for performance analysis.
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.