融合门控单元与多头自注意力机制的特征自动交互推荐算法  

Feature automatic interactive recommendation algorithm integrating gating unitand multi⁃head self⁃attention mechanism

作　　者：喻金平[1] 李钰 姚炫辰 罗琛 YU Jinping;LI Yu;YAO Xuanchen;LUO Chen(School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学信息工程学院,江西赣州341000

出　　处：《现代电子技术》2023年第23期126-132,共7页Modern Electronics Technique

基　　金：中央引导地方科技发展专项资金(20201ZDI03003)。

摘　　要：为了解决推荐算法中使用手工制作、特征工程等方式枚举所有的特征组合不但会带来巨大的存储空间和计算成本,而且无用的特征交互会引入噪声使模型训练过程复杂化的问题,文中提出融合多头自注意力机制的特征自动交互推荐算法。该算法首先利用门控机制对输入特征进行初次筛选;然后将特征送入多头自注意力机制中,选取关键特征进行不同阶的组合;最后利用残差网络进行特征融合输出预测结果。该算法能有效地提高预测结果的准确性,同时具有良好的解释性。In recommended algorithms,enumerating all feature combinations by means of manual production and feature engineering will result in huge storage space and computational costs.In addition,irrelevant feature interaction will bring noise,which will complicate the model training process,so a feature automatic interactive recommendation algorithm integrating multi⁃head self⁃attention mechanism is proposed.In the algorithm,a gating mechanism is used to filter the input features,the features are fed into the multi⁃head self⁃attention mechanism,and the key features are selected for combination of different stages.Finally,a residual network is utilized for feature fusion and output of prediction results.The proposed algorithm can effectively improve the prediction accuracy while maintaining good interpretability.

关 键 词：门控单元 自动特征交互 多头自注意力机制 推荐算法 特征组合 可解释性 

分 类 号：TN911.1-34[电子电信—通信与信息系统] TP301.6[电子电信—信息与通信工程]