Conversion rate (CVR) prediction models play a vital role in recommendation and advertising systems. Recent research on multi-scenario recommendation shows that learning a unified model to serve multiple scenarios is effective for improving overall performance. However, it remains challenging to improve model prediction performance across scenarios at low model parameter cost, and current solutions are hard to robustly model multi-scenario diversity. In this paper, we propose MI-DPG for the multi-scenario CVR prediction, which learns scenario-conditioned dynamic model parameters for each scenario in a more efficient and effective manner. Specifically, we introduce an auxiliary network to generate scenario-conditioned dynamic weighting matrices, which are obtained by combining decomposed scenario-specific and scenario-shared low-rank matrices with parameter efficiency. For each scene, weighting the backbone model parameters by the weighting matrix helps to specialize the model parameters for different scenarios. It can not only modulate the complete parameter space of the backbone model but also improve the model effectiveness. Furthermore, we design a mutual information regularization to enhance the diversity of model parameters across different scenarios by maximizing the mutual information between the scenario-aware input and the scene-conditioned dynamic weighting matrix. Experiments from three real-world datasets show that MI-DPG significantly outperforms previous multi-scenario recommendation models.

翻译：转化率（CVR）预测模型在推荐与广告系统中扮演着关键角色。近期关于多场景推荐的研究表明，学习一个统一模型以服务于多个场景，对于提升整体性能具有显著效果。然而，如何在低模型参数成本下提高跨场景的模型预测性能仍具挑战性，且现有方案难以稳健地对多场景多样性进行建模。本文提出MI-DPG用于多场景CVR预测，该方法以更高效且有效的方式为每个场景学习场景条件动态模型参数。具体而言，我们引入一个辅助网络来生成场景条件动态权重矩阵，该矩阵通过组合可分解的场景特定与场景共享低秩矩阵获得，并兼顾参数效率。对于每个场景，利用该权重矩阵对骨干模型参数进行加权，有助于为不同场景定制模型参数。这不仅能够调制骨干模型的完整参数空间，还能提升模型有效性。此外，我们设计了互信息正则化，通过最大化场景感知输入与场景条件动态权重矩阵之间的互信息，增强不同场景间模型参数的多样性。基于三个真实数据集的实验表明，MI-DPG显著优于以往的多场景推荐模型。