mlsanityData to 14 Oct 2026

FINT: Field-aware INTeraction Neural Network For CTR Prediction

arXiv 2107.01999 ↗First posted 5 Jul 2021cs.IR cs.CVcited by 1

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Abstract (arXiv)

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 released our code as well as reference implementation at: https://github.com/zhishan01/FINT.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 5 Jul 2021.

Builds on

19 of 27 references.

References · 19

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7 more
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Citing papers per year: 2022: 1.

1 paper.

  1. 2022PHN: Parallel heterogeneous network with soft gating for CTR predictioncited by 1