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As a critical component for online advertising and marking, click-throughrate (CTR) prediction has draw lots of attentions from both industry andacademia field. Recently, the deep learning has become the mainstreammethodological choice for CTR. Despite of sustainable efforts have been made,existing approaches still pose several challenges. On the one hand, high-orderinteraction between the features is under-explored. On the other hand,high-order interactions may neglect the semantic information from the low-orderfields. In this paper, we proposed a novel prediction method, named FINT, thatemploys the Field-aware INTeraction layer which captures high-order featureinteractions while retaining the low-order field information. To empiricallyinvestigate the effectiveness and robustness of the FINT, we perform extensiveexperiments on the three realistic databases: KDD2012, Criteo and Avazu. Theobtained results demonstrate that the FINT can significantly improve theperformance compared to the existing methods, without increasing the amount ofcomputation required. Moreover, the proposed method brought about 2.72\%increase to the advertising revenue of a big online video app through A/Btesting. To better promote the research in CTR field, we released our code aswell as reference implementation at: https://github.com/zhishan01/FINT.

FINT: Field-aware INTeraction Neural Network For CTR Prediction