典型文献
Predictive Model of Live Shopping Interest Degree Based on Eye Movement Characteristics and Deep Factorization Machine
文献摘要：
In the live broadcast process, eye movement characteristics can reflect people's attention to the product. However, the existing interest degree predictive model research does not consider the eye movement characteristics. In order to obtain the users' interest in the product more effectively, we will consider the key eye movement indicators. We first collect eye movement characteristics based on the self-developed data processing algorithm fast discriminative model prediction for tracking (FDIMP), and then we add data dimensions to the original data set through information filling. In addition, we apply the deep factorization machine (DeepFM) architecture to simultaneously learn the combination of low-level and high-level features. In order to effectively learn important features and emphasize relatively important features, the multi-head attention mechanism is applied in the interest model. The experimental results on the public data set Criteo show that, compared with the original DeepFM algorithm, the area under curve ( AUC) value was improved by up to 9.32％.
文献关键词：
中图分类号：
作者姓名：
SHI Xiujin;LI Hao;SHI Hang;WANG Shaoyu;SUN Guohao
作者机构：
School of Computer Science and Technology,Donghua University,Shanghai 201620,China
文献出处：
引用格式：
[1]SHI Xiujin;LI Hao;SHI Hang;WANG Shaoyu;SUN Guohao-.Predictive Model of Live Shopping Interest Degree Based on Eye Movement Characteristics and Deep Factorization Machine)[J].东华大学学报（英文版）,2022(04):353-360
A类：
FDIMP
B类：
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AB值：
0.621034
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
