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.

在当前的推荐系统中，时间数据的转变构成了一个重要挑战。为应对数据转变，我们设计了一种使用检索技术的框架来利用转变的数据来训练相关性网络，并使用知识蒸馏将相关性网络的知识转移到参数化模块中，即搜索蒸馏模块。我们将这整个过程称为检索与蒸馏范式（RAD）。在未来的研究方向中，我们旨在利用 RAD 将更多种类的数据纳入 CTR 模型，并提升蒸馏方法的性能。