  Recommender systems rely heavily on increasing computation resources to
improve their business goal. By deploying computation-intensive models and
algorithms, these systems are able to inference user interests and exhibit
certain ads or commodities from the candidate set to maximize their business
goals. However, such systems are facing two challenges in achieving their
goals. On the one hand, facing massive online requests, computation-intensive
models and algorithms are pushing their computation resources to the limit. On
the other hand, the response time of these systems is strictly limited to a
short period, e.g. 300 milliseconds in our real system, which is also being
exhausted by the increasingly complex models and algorithms.
  In this paper, we propose the computation resource allocation solution (CRAS)
that maximizes the business goal with limited computation resources and
response time. We comprehensively illustrate the problem and formulate such a
problem as an optimization problem with multiple constraints, which could be
broken down into independent sub-problems. To solve the sub-problems, we
propose the revenue function to facilitate the theoretical analysis, and obtain
the optimal computation resource allocation strategy. To address the
applicability issues, we devise the feedback control system to help our
strategy constantly adapt to the changing online environment. The effectiveness
of our method is verified by extensive experiments based on the real dataset
from Taobao.com. We also deploy our method in the display advertising system of
Alibaba. The online results show that our computation resource allocation
solution achieves significant business goal improvement without any increment
of computation cost, which demonstrates the efficacy of our method in real
industrial practice.
