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

MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in  Recommendation Systems

Recent work in recommender systems mainly focuses on fairness in
recommendations as an important aspect of measuring recommendations quality. A
fairness-aware recommender system aims to treat different user groups
similarly. Relevant work on user-oriented fairness highlights the
discriminative behavior of fairness-unaware recommendation algorithms towards a
certain user group, defined based on users' activity level. Typical solutions
include proposing a user-centered fairness re-ranking framework applied on top
of a base ranking model to mitigate its unfair behavior towards a certain user
group i.e., disadvantaged group. In this paper, we re-produce a user-oriented
fairness study and provide extensive experiments to analyze the dependency of
their proposed method on various fairness and recommendation aspects, including
the recommendation domain, nature of the base ranking model, and user grouping
method. Moreover, we evaluate the final recommendations provided by the
re-ranking framework from both user- (e.g., NDCG, user-fairness) and item-side
(e.g., novelty, item-fairness) metrics. We discover interesting trends and
trade-offs between the model's performance in terms of different evaluation
metrics. For instance, we see that the definition of the
advantaged/disadvantaged user groups plays a crucial role in the effectiveness
of the fairness algorithm and how it improves the performance of specific base
ranking models. Finally, we highlight some important open challenges and future
directions in this field. We release the data, evaluation pipeline, and the
trained models publicly on this https URL.

研究在推荐系统中的公平性问题，提出一种基于用户为导向的公平性重排框架来缓解基于排名的不公平行为，发现不同用户组的定义在算法的有效性和特定排名模型的性能改进方面起到至关重要的作用。