  Promotions are becoming more important and prevalent in e-commerce to attract
customers and boost sales, leading to frequent changes of occasions, which
drives users to behave differently. In such situations, most existing
Click-Through Rate (CTR) models can't generalize well to online serving due to
distribution uncertainty of the upcoming occasion. In this paper, we propose a
novel CTR model named MOEF for recommendations under frequent changes of
occasions. Firstly, we design a time series that consists of occasion signals
generated from the online business scenario. Since occasion signals are more
discriminative in the frequency domain, we apply Fourier Transformation to
sliding time windows upon the time series, obtaining a sequence of frequency
spectrum which is then processed by Occasion Evolution Layer (OEL). In this
way, a high-order occasion representation can be learned to handle the online
distribution uncertainty. Moreover, we adopt multiple experts to learn feature
representations from multiple aspects, which are guided by the occasion
representation via an attention mechanism. Accordingly, a mixture of feature
representations is obtained adaptively for different occasions to predict the
final CTR. Experimental results on real-world datasets validate the superiority
of MOEF and online A/B tests also show MOEF outperforms representative CTR
models significantly.
