The fintech PicPay offers a wide range of financial services to its 30
million monthly active users, with more than 50 thousand items recommended in
the PicPay mobile app. In this scenario, promoting specific items that are
strategic to the company can be very challenging. In this work, we present a
Switching Hybrid Recommender System that combines two algorithms to effectively
promote items without negatively impacting the user's experience. The results
of our A/B tests show an uplift of up to 3.2\% when compared to a default
recommendation strategy.

PicPay 利用 Switching Hybrid Recommender System 进行 A/B 测试来提升推荐策略，结果显示相比默认策略，用户体验有了 3.2% 的提升。

混合推荐系统在 PicPay 的金融服务推广中的应用

Rethinking Financial Service Promotion With Hybrid Recommender Systems  at PicPay

Contextual bandits algorithms aim to choose the optimal arm with the highest
reward out of a set of candidates based on the contextual information. Various
bandit algorithms have been applied to real-world applications due to their
ability of tackling the exploitation-exploration dilemma. Motivated by online
recommendation scenarios, in this paper, we propose a framework named Graph
Neural Bandits (GNB) to leverage the collaborative nature among users empowered
by graph neural networks (GNNs). Instead of estimating rigid user clusters as
in existing works, we model the "fine-grained" collaborative effects through
estimated user graphs in terms of exploitation and exploration respectively.
Then, to refine the recommendation strategy, we utilize separate GNN-based
models on estimated user graphs for exploitation and adaptive exploration.
Theoretical analysis and experimental results on multiple real data sets in
comparison with state-of-the-art baselines are provided to demonstrate the
effectiveness of our proposed framework.

通过图神经网络，本论文提出了一种名为图神经赌博（GNB）的框架，旨在利用用户之间的协作特性来提高在线推荐系统的性能，并通过理论分析和实证研究验证了我们提出的框架的有效性。