Fri May 29 06:59:50 UTC 2020

test phlog post 2

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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.