  Rich user behavior data has been proven to be of great value for
click-through rate prediction tasks, especially in industrial applications such
as recommender systems and online advertising. Both industry and academy have
paid much attention to this topic and propose different approaches to modeling
with long sequential user behavior data. Among them, memory network based model
MIMN proposed by Alibaba, achieves SOTA with the co-design of both learning
algorithm and serving system. MIMN is the first industrial solution that can
model sequential user behavior data with length scaling up to 1000. However,
MIMN fails to precisely capture user interests given a specific candidate item
when the length of user behavior sequence increases further, say, by 10 times
or more. This challenge exists widely in previously proposed approaches. In
this paper, we tackle this problem by designing a new modeling paradigm, which
we name as Search-based Interest Model (SIM). SIM extracts user interests with
two cascaded search units: (i) General Search Unit acts as a general search
from the raw and arbitrary long sequential behavior data, with query
information from candidate item, and gets a Sub user Behavior Sequence which is
relevant to candidate item; (ii) Exact Search Unit models the precise
relationship between candidate item and SBS. This cascaded search paradigm
enables SIM with a better ability to model lifelong sequential behavior data in
both scalability and accuracy. Apart from the learning algorithm, we also
introduce our hands-on experience on how to implement SIM in large scale
industrial systems. Since 2019, SIM has been deployed in the display
advertising system in Alibaba, bringing 7.1\% CTR and 4.4\% RPM lift, which is
significant to the business. Serving the main traffic in our real system now,
SIM models user behavior data with maximum length reaching up to 54000, pushing
SOTA to 54x.
