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 be-
haviors to build an accurate CTR prediction model. However, as
the users accumulate more and more behavioral data on the plat-
forms, 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 be-
haviors 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 superi-
ority and efficacy of our proposed framework and models.
