DFILAN: Domain-Based Feature Interactions Learning via Attention Networks for CTR Prediction

2021 ◽  
pp. 500-515
Author(s):  
Yongliang Han ◽  
Yingyuan Xiao ◽  
Hongya Wang ◽  
Wenguang Zheng ◽  
Ke Zhu
Keyword(s):  
Attention Networks ◽  
Feature Interactions

scholarly journals Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

2017 ◽  
Author(s):  
Jun Xiao ◽  
Hao Ye ◽  
Xiangnan He ◽  
Hanwang Zhang ◽  
Fei Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Feature Interaction ◽  
Model Parameters ◽  
Learning Approach ◽  
Attention Networks ◽  
Feature Interactions ◽  
Factorization Machine ◽  
Real World Datasets ◽  
Novel Model

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github.com/hexiangnan/attentional_factorization_machine

Cross-modality co-attention networks for visual question answering

2021 ◽  
Author(s):  
Dezhi Han ◽  
Shuli Zhou ◽  
Kuan Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Question Answering ◽  
Attention Networks ◽  
Visual Question Answering

scholarly journals Superpixel Image Classification with Graph Attention Networks

2020 ◽  
Author(s):  
Pedro H. C. Avelar ◽  
Anderson R. Tavares ◽  
Thiago L. T. da Silveira ◽  
Cliudio R. Jung ◽  
Luis C. Lamb
Keyword(s):  
Image Classification ◽  
Attention Networks

scholarly journals Social exclusion increases the executive function of attention networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Huoyin Zhang ◽  
Shiyunmeng Zhang ◽  
Jiachen Lu ◽  
Yi Lei ◽  
Hong Li
Keyword(s):  
Social Exclusion ◽  
Executive Control ◽  
Brain Regions ◽  
Negative Influence ◽  
Future Research ◽  
Research Attention ◽  
Attention Network ◽  
Attention Networks ◽  
The Social ◽  
And Control

AbstractPrevious studies in humans have shown that brain regions activating social exclusion overlap with those related to attention. However, in the context of social exclusion, how does behavioral monitoring affect individual behavior? In this study, we used the Cyberball game to induce the social exclusion effect in a group of participants. To explore the influence of social exclusion on the attention network, we administered the Attention Network Test (ANT) and compared results for the three subsystems of the attention network (orienting, alerting, and executive control) between exclusion (N = 60) and inclusion (N = 60) groups. Compared with the inclusion group, the exclusion group showed shorter overall response time and better executive control performance, but no significant differences in orienting or alerting. The excluded individuals showed a stronger ability to detect and control conflicts. It appears that social exclusion does not always exert a negative influence on individuals. In future research, attention to network can be used as indicators of social exclusion. This may further reveal how social exclusion affects individuals' psychosomatic mechanisms.

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