  Click-through rate (CTR) prediction is a critical task in online advertising
systems. Existing works mainly address the single-domain CTR prediction problem
and model aspects such as feature interaction, user behavior history and
contextual information. Nevertheless, ads are usually displayed with natural
content, which offers an opportunity for cross-domain CTR prediction. In this
paper, we address this problem and leverage auxiliary data from a source domain
to improve the CTR prediction performance of a target domain. Our study is
based on UC Toutiao (a news feed service integrated with the UC Browser App,
serving hundreds of millions of users daily), where the source domain is the
news and the target domain is the ad. In order to effectively leverage news
data for predicting CTRs of ads, we propose the Mixed Interest Network (MiNet)
which jointly models three types of user interest: 1) long-term interest across
domains, 2) short-term interest from the source domain and 3) short-term
interest in the target domain. MiNet contains two levels of attentions, where
the item-level attention can adaptively distill useful information from clicked
news / ads and the interest-level attention can adaptively fuse different
interest representations. Offline experiments show that MiNet outperforms
several state-of-the-art methods for CTR prediction. We have deployed MiNet in
UC Toutiao and the A/B test results show that the online CTR is also improved
substantially. MiNet now serves the main ad traffic in UC Toutiao.
