基于DNN与注意力机制的推荐算法模型

Recommendation Algorithm Model Based on DNN and Attention Mechanism

为解决因子分解机在提取高阶组合特征的缺陷问题,更好地学习到更多有用的特征信息,尝试用因子分解机提取交叉特征,并结合注意力网络、深度神经网络和多头自注意力机制等方法,从低、高阶组合特征中学习关键特征信息,最后根据不同阶的组合特征的重要性加权融合得到结果,以预估广告点击率。实验主要基于广告数据集Criteo展开,并在MovieLens数据集上进行类比实验,验证所提出算法模型的有效性,实验结果显示,本文提出的算法模型相较于基准模型,在2个数据集上的AUC指标提升分别有2.32个百分点和0.4个百分点。

In order to solve the defect of factorization machine in extracting high-order combination features and learn more use‐ful feature information better,this paper attempts to use factorization machine to extract cross-feature and learn key feature infor‐mation from low and high-order combination features by combining attention network,deep neural network,multi-head self attention mechanism and other methods.Finally,the weighted fusion results were obtained according to the importance of the combination features of different orders,and the click-through rate of advertisements was estimated.The experiment was mainly carried out based on the advertising data set Criteo,and the analogy experiment was carried out with MovieLens data set to verify the effectiveness of the proposed algorithm model.The experimental results showed that compared with the benchmark model,in the two data sets,the AUC index increased by 2.32 percntage points and 0.4 percntage points.