天津职业技术师范大学信息技术工程学院;
针对XdeepFM模型将嵌入特征直接送入特征交互网络后使网络获取到大量冗余特征，同时模型二阶特征交叉能力不足的问题，采用图神经网络和注意力机制优化了模型的特征提取能力，并添加因子分解机算法提高了模型的二阶交叉能力，提出了GA-XdeepFM-FM新模型，并在公开数据集Criteo和Avazu上进行实验。实验结果表明：相比较XdeepFM算法以及经典算法，模型的AUC和Logloss皆有显著提高，证明了新模型在特征提取、特征交叉和预测广告点击率方面的有效性，为相关应用提供了更为有效的解决方案。
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基本信息:
DOI：10.19573/j.issn2095-0926.202303008
中图分类号:TP391.3;TP183
引用信息:
[1]王英桥,武志峰.融合图神经网络与注意力机制的XdeepFM改进算法[J].天津职业技术师范大学学报,2023,33(03):42-48.DOI:10.19573/j.issn2095-0926.202303008.
基金信息:
教育部人文社会科学研究一般项目（22YJC870018）