Abstract
In current recommendation systems, temporal data shift poses a significant challenge. The presence of data shift prevents the system from simply enhancing the CTR model's adaptability to new data by adding more training data. We observed that although the correlation between features and labels in recommendation systems changes over time, if a fixed search space is established, the relationship between the data and the search space remains invariant. Therefore, we designed a framework that uses retrieval techniques to leverage shifting data for training a relevance network. However, due to the use of BM25 as a retrieval method, this framework is challenging to deploy in online recommendation systems. We then designed a distillation method using knowledge distillation to transfer knowledge from the relevance network to a parameterized module, the search-distill module. We refer to this entire process as the Retrieval and Distill paradigm (RAD). With the RAD paradigm, we have an effective method for leveraging shifting data to enhance the performance of CTR models. In future research directions, we aim to incorporate a wider variety of data into the CTR model using RAD. On the other hand, enhancing the performance of the distillation method is also a significant area of focus.
Abstract (translated)
在当前的推荐系统中，时间数据变化是一个重大的挑战。数据的变化阻止了系统通过添加更多训练数据来简单地提高CTR模型的适应性。我们观察到，尽管推荐系统中的特征与标签之间的相关性会随着时间的变化而变化，但只要建立了一个固定的搜索空间，数据与搜索空间之间的关系就会保持不变。因此，我们设计了一个使用检索技术利用 shifting 数据来训练相关网络的框架。然而，由于使用 BM25 作为检索方法，这个框架在在线推荐系统中很难部署。然后，我们使用知识蒸馏技术设计了一个馏方法，将来自相关网络的知识传递给参数化模块，即搜索-蒸馏模块。我们将这个整个过程称为检索和蒸馏范式（RAD）。通过 RAD 范式，我们有一种有效的利用移动物品增强CTR模型性能的方法。在未来的研究方向中，我们旨在通过 RAD 将更广泛的数据集成到CTR模型中。另一方面，提高蒸馏方法的效果也是一个重要的关注点。
URL
https://arxiv.org/abs/2404.15678
