论文标题
多阶段推荐系统中的神经重新排列：评论
Neural Re-ranking in Multi-stage Recommender Systems: A Review
论文作者
论文摘要
作为多阶段推荐系统（MRS）的最后阶段，重新排名直接通过重新排列输入排名列表来直接影响用户体验和满意度，从而在MRS中起着至关重要的作用。随着深度学习的进步，神经重新排列已成为一个热门话题，并广泛应用于工业应用中。这篇评论旨在将重新排行的算法整合到更广泛的情况下，并为未来研究的更全面的解决方案铺平方法。为此，我们首先介绍了有关神经重新排列的当前方法的分类法。然后，我们根据其目标对这些方法以及历史发展进行描述。还讨论和比较了网络结构，个性化和复杂性。接下来，我们提供主要神经重新排列模型的基准，并定量分析其重新排列的性能。最后，审查以讨论该领域的未来前景进行了讨论。本评论中讨论的论文列表，基准数据集，我们的重新排列库库以及详细的参数设置，请访问https://github.com/librerank-community/librerank。
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.
