Click-through rate (CTR) prediction plays a key role in modern online
personalization services. In practice, it is necessary to capture user's
drifting interests by modeling sequential user behaviors to build an accurate
CTR prediction model. However, as the users accumulate more and more behavioral
data on the platforms, it becomes non-trivial for the sequential models to make
use of the whole behavior history of each user. First, directly feeding the
long behavior sequence will make online inference time and system load
infeasible. Second, there is much noise in such long histories to fail the
sequential model learning. The current industrial solutions mainly truncate the
sequences and just feed recent behaviors to the prediction model, which leads
to a problem that sequential patterns such as periodicity or long-term
dependency are not embedded in the recent several behaviors but in far back
history. To tackle these issues, in this paper we consider it from the data
perspective instead of just designing more sophisticated yet complicated models
and propose User Behavior Retrieval for CTR prediction (UBR4CTR) framework. In
UBR4CTR, the most relevant and appropriate user behaviors will be firstly
retrieved from the entire user history sequence using a learnable search
method. These retrieved behaviors are then fed into a deep model to make the
final prediction instead of simply using the most recent ones. It is highly
feasible to deploy UBR4CTR into industrial model pipeline with low cost.
Experiments on three real-world large-scale datasets demonstrate the
superiority and efficacy of our proposed framework and models.

本论文提出了一种基于数据检索的用户行为检索框架（UBR4CTR），通过可学习的搜索方法从整个用户历史序列中检索出最相关和最适当的用户行为，然后将这些检索到的行为馈入深度模型进行最终预测，该框架能够在低成本下被高度可行地部署到工业模型流水线中，三个实际大规模数据集上的实验证明了该框架的优越性和功效。