计算机工程与科学2025，Vol.47Issue(5)：902-911,10.DOI:10.3969/j.issn.1007-130X.2025.05.014
基于注意力机制的特征融合推荐模型
A feature fusion recommendation model based on attention mechanism
摘要
Abstract
Addressing the current challenges in recommendation systems,which include difficulties in obtaining feature information and the lack of effective methods to represent the weights of feature in-formation,this study proposes a recommendation model based on the attention mechanism and feature fusion,named FFADeepCF_SPS.Firstly,to address the inadequate feature representation,the Factori-zation Machines(FM)are employed to fuse features,extending them from one-dimensional to high-dimensional space to obtain low-order feature representations.Subsequently,a Deep Neural Network(DNN)is used to learn high-order features,and the two types of features are combined through a fully connected layer to obtain the required feature representation.Secondly,to address the issue of excessive weight skewing in the single-head attention mechanism,a multi-head attention mechanism is adopted,where the input is divided into multiple single heads to calculate their attention weights separately.The results from each head are then concatenated through a linear transformation to obtain the final output.Finally,combining the above two points,a recommendation model based on the attention mechanism and feature fusion is constructed.To validate the effectiveness of the model,comparative experiments and ablation studies are conducted on four public datasets against baseline models such as GMF,DeepCF_SPS,and CNN-BiLSTM.The experimental results show that the proposed model outperforms the base-line models in terms of MSE,RMSE,and MAE evaluation metrics across datasets of different sizes.关键词
注意力机制/特征融合/推荐模型/评分预测Key words
attention mechanism/feature fusion/recommendation model/score prediction分类
信息技术与安全科学引用本文复制引用
马汉达,李腾飞..基于注意力机制的特征融合推荐模型[J].计算机工程与科学,2025,47(5):902-911,10.基金项目
镇江市重点研发计划(GY2023034) （GY2023034）
