In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness. Specifically, our Sequential Greedy Algorithm (SGA) is efficient enough (linear time complexity) for large-scale production recommendation engines. It achieved a lift of $+6\%$ to $ +10\%$ offline Area Under the receiver operating characteristic Curve (AUC) which is mainly due to explicitly modeling mutual influences among items of a list, and leveraging the second pass ranking scores of multiple objectives. In addition, we have generalized the offline replay theory to multi-slot re-ranking scenarios, with trade-offs among multiple objectives. The offline replay results can be further improved by Pareto Optimality. Moreover, we've built a multi-slot re-ranking simulator based on OpenAI Gym integrated with the Ray framework. It can be easily configured for different assumptions to quickly benchmark both reinforcement learning and supervised learning algorithms.

我们提出了一个通用的基于模型的重新排序框架，MultiSlot ReRanker，它同时优化相关性、多样性和新颖性。具体来说，我们的顺序贪心算法 (SGA) 在大规模生产推荐引擎中具有足够高的效率 (线性时间复杂度)。它在离线接收器工作特性曲线 (AUC) 上实现了 +6% 到 +10% 的提升，这主要是由于显性地建模列表中项目之间的相互影响，并利用多个目标的第二次排序分数。此外，我们将离线重放理论推广到多槽重新排序场景中，具有多个目标之间的权衡。通过 Pareto 最优性可以进一步改进离线重放结果。此外，我们还基于集成了 Ray 框架的 OpenAI Gym 构建了一个多槽重新排序模拟器，可以轻松配置不同的假设，快速评估强化学习和监督学习算法。

多槽位重新排名器：推荐系统中的通用基于模型的重新排名框架