Click-through rate (CTR) prediction is critical for industrial applications
such as recommender system and online advertising. Practically, it plays an
important role for CTR modeling in these applications by mining user interest
from rich historical behavior data. Driven by the development of deep learning,
deep CTR models with ingeniously designed architecture for user interest
modeling have been proposed, bringing remarkable improvement of model
performance over offline metric.However, great efforts are needed to deploy
these complex models to online serving system for realtime inference, facing
massive traffic request. Things turn to be more difficult when it comes to long
sequential user behavior data, as the system latency and storage cost increase
approximately linearly with the length of user behavior sequence. In this
paper, we face directly the challenge of long sequential user behavior modeling
and introduce our hands-on practice with the co-design of machine learning
algorithm and online serving system for CTR prediction task. Theoretically, the
co-design solution of UIC and MIMN enables us to handle the user interest
modeling with unlimited length of sequential behavior data. Comparison between
model performance and system efficiency proves the effectiveness of proposed
solution. To our knowledge, this is one of the first industrial solutions that
are capable of handling long sequential user behavior data with length scaling
up to thousands. It now has been deployed in the display advertising system in
Alibaba.

本研究通过机器学习算法与在线服务系统的共同设计，提出了处理长序列用户行为数据的解决方案，能够有效地处理超过千级的长序列用户行为数据，提升了模型性能和系统效率。