FINT: Field-Aware Interaction Neural Network for Click-Through Rate Prediction

As a critical component for online advertising and marketing, click-through rate (CTR) prediction has drawn lots of attention from both industry and academia. Recently, deep learning has become the mainstream methodological choice for CTR. Despite sustainable efforts have been made, existing approac...

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Bibliographic Details
Main Authors: Zhao, Zhishan, Yang, Sen, Liu, Guohui, Feng, Dawei, Xu, Kele
Format: Conference Proceeding
Language:English
Subjects:
Click-through Rate
Deep learning
Field-Aware Interaction
High-order Feature Interactions
Industries
Neural networks
Predictive models
Robustness
Semantics
Signal processing
Online Access:Request full text
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Summary:As a critical component for online advertising and marketing, click-through rate (CTR) prediction has drawn lots of attention from both industry and academia. Recently, deep learning has become the mainstream methodological choice for CTR. Despite 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 explicitly 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 iQIYI, 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.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9747247