As a critical component for online advertising and marking, click-through
rate (CTR) prediction has draw lots of attentions from both industry and
academia field. Recently, the deep learning has become the mainstream
methodological choice for CTR. Despite of sustainable efforts have been made,
existing approaches still pose several challenges. On the one hand, high-order
interaction between the features is under-explored. On the other hand,
high-order interactions may neglect the semantic information from the low-order
fields. In this paper, we proposed a novel prediction method, named FINT, that
employs the Field-aware INTeraction layer which captures high-order feature
interactions while retaining the low-order field information. To empirically
investigate the effectiveness and robustness of the FINT, we perform extensive
experiments on the three realistic databases: KDD2012, Criteo and Avazu. The
obtained results demonstrate that the FINT can significantly improve the
performance compared to the existing methods, without increasing the amount of
computation required. Moreover, the proposed method brought about 2.72\%
increase to the advertising revenue of a big online video app through A/B
testing. To better promote the research in CTR field, we will release our code
as well as reference implementation of those baseline models in the final
version.Comment: 5 pages, Submitted to CIKM 202