Data bias, e.g., popularity impairs the dynamics of two-sided markets within
recommender systems. This overshadows the less visible but potentially
intriguing long-tail items that could capture user interest. Despite the
abundance of research surrounding this issue, it still poses challenges and
remains a hot topic in academic circles. Along this line, in this paper, we
developed a re-ranking approach in dynamic settings with fair-exposure
optimization driven by strategic agents. Designed for the producer side, the
execution of agents assumes content creators can modify item features based on
strategic incentives to maximize their exposure. This iterative process entails
an end-to-end optimization, employing differentiable ranking operators that
simultaneously target accuracy and fairness. Joint objectives ensure the
performance of recommendations while enhancing the visibility of tail items. We
also leveraged the performativity nature of predictions to illustrate how
strategic learning influences content creators to shift towards fairness
efficiently, thereby incentivizing features of tail items. Through
comprehensive experiments on both public and industrial datasets, we have
substantiated the effectiveness and dominance of the proposed method especially
on unveiling the potential of tail items.

在该研究中，我们开发了一种基于战略代理人的公平曝光优化动态排序方法，旨在解决推荐系统中的数据偏差和长尾物品可见性的问题。通过使用可微分的排序算子同时优化准确性和公平性，我们确保了推荐性能的同时增强了长尾物品的可见性，实验证明了该方法的有效性和优越性。

Performative Debias with Fair-exposure Optimization Driven by Strategic  Agents in Recommender Systems

Considering the prevalence of the power-law distribution in user-item
networks, hyperbolic space has attracted considerable attention and achieved
impressive performance in the recommender system recently. The advantage of
hyperbolic recommendation lies in that its exponentially increasing capacity is
well-suited to describe the power-law distributed user-item network whereas the
Euclidean equivalent is deficient. Nonetheless, it remains unclear which kinds
of items can be effectively recommended by the hyperbolic model and which
cannot. To address the above concerns, we take the most basic recommendation
technique, collaborative filtering, as a medium, to investigate the behaviors
of hyperbolic and Euclidean recommendation models. The results reveal that (1)
tail items get more emphasis in hyperbolic space than that in Euclidean space,
but there is still ample room for improvement; (2) head items receive modest
attention in hyperbolic space, which could be considerably improved; (3) and
nonetheless, the hyperbolic models show more competitive performance than
Euclidean models. Driven by the above observations, we design a novel learning
method, named hyperbolic informative collaborative filtering (HICF), aiming to
compensate for the recommendation effectiveness of the head item while at the
same time improving the performance of the tail item. The main idea is to adapt
the hyperbolic margin ranking learning, making its pull and push procedure
geometric-aware, and providing informative guidance for the learning of both
head and tail items. Extensive experiments back up the analytic findings and
also show the effectiveness of the proposed method. The work is valuable for
personalized recommendations since it reveals that the hyperbolic space
facilitates modeling the tail item, which often represents user-customized
preferences or new products.

本研究探讨了在超几何空间内，哪些用户产品可以有效推荐，设计了一种新的学习方法 HICF，针对尾部和头部物品推荐效果不佳的问题，通过改进超几何边际排序学习的过程，提供信息指导来明确推荐目标，并在大量实验中验证了该方法的有效性。

HICF: 双曲信息协作过滤

HICF: Hyperbolic Informative Collaborative Filtering

Sequential recommendation techniques provide users with product
recommendations fitting their current preferences by handling dynamic user
preferences over time. Previous studies have focused on modeling sequential
dynamics without much regard to which of the best-selling products (i.e., head
items) or niche products (i.e., tail items) should be recommended. We
scrutinize the structural reason for why tail items are barely served in the
current sequential recommendation model, which consists of an item-embedding
layer, a sequence-modeling layer, and a recommendation layer. Well-designed
sequence-modeling and recommendation layers are expected to naturally learn
suitable item embeddings. However, tail items are likely to fall short of this
expectation because the current model structure is not suitable for learning
high-quality embeddings with insufficient data. Thus, tail items are rarely
recommended. To eliminate this issue, we propose a framework called CITIES,
which aims to enhance the quality of the tail-item embeddings by training an
embedding-inference function using multiple contextual head items so that the
recommendation performance improves for not only the tail items but also for
the head items. Moreover, our framework can infer new-item embeddings without
an additional learning process. Extensive experiments on two real-world
datasets show that applying CITIES to the state-of-the-art methods improves
recommendation performance for both tail and head items. We conduct an
additional experiment to verify that CITIES can infer suitable new-item
embeddings as well.

该研究提出了一种名为 CITIES 的框架，旨在通过训练一个嵌入 - 推理函数来提高尾部项目的嵌入质量，以改善不仅尾部项目而且头部项目的推荐性能，并且可以推断新项目的嵌入。在两个真实世界数据集上的广泛实验表明，将 CITIES 应用于最先进的方法可以提高尾部和头部项目的推荐性能。