  "Creativity is the heart and soul of advertising services". Effective
creatives can create a win-win scenario: advertisers can reach target users and
achieve marketing objectives more effectively, users can more quickly find
products of interest, and platforms can generate more advertising revenue. With
the advent of AI-Generated Content, advertisers now can produce vast amounts of
creative content at a minimal cost. The current challenge lies in how
advertising systems can select the most pertinent creative in real-time for
each user personally. Existing methods typically perform serial ranking of ads
or creatives, limiting the creative module in terms of both effectiveness and
efficiency. In this paper, we propose for the first time a novel architecture
for online parallel estimation of ads and creatives ranking, as well as the
corresponding offline joint optimization model. The online architecture enables
sophisticated personalized creative modeling while reducing overall latency.
The offline joint model for CTR estimation allows mutual awareness and
collaborative optimization between ads and creatives. Additionally, we optimize
the offline evaluation metrics for the implicit feedback sorting task involved
in ad creative ranking. We conduct extensive experiments to compare ours with
two state-of-the-art approaches. The results demonstrate the effectiveness of
our approach in both offline evaluations and real-world advertising platforms
online in terms of response time, CTR, and CPM.
