增强非线性特征提取的时间间隔感知序列推荐  

Time Interval Aware Sequence Recommendation of Enhancing Nonlinear Feature Extraction

作　　者：宁昱霖 NING Yulin(School of Computer Science and Engineering,Anhui University of Science&Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001

出　　处：《现代信息科技》2022年第7期85-87,90,共4页Modern Information Technology

摘　　要：针对基于时间间隔的序列推荐模型存在的非线性特征提取不充分问题,提出了增强非线性特征提取的时间间隔感知序列推荐模型,改进了已有的推荐模型。用多层线性层代替传统的基于时间间隔的序列推荐模型中的前馈神经网络,增强模型对于深层次项目交互信息的捕捉能力。在三个公开数据集上验证了所提出模型的有效性。评估指标平均提高1.9%,最高提升5.2%。Aiming at the problem of existing insufficient nonlinear feature extraction based on the time interval sequence recommendation model,this paper proposes a time interval aware sequence recommendation model of enhancing nonlinear feature extraction,and it improves the existing recommendation model.The feed forward neural network based on the traditional time interval sequence recommendation model is replaced by multi-layer linear layer to enhance the model’s ability to capture the deep level item interaction information.The validity of the proposed model is verified on three public datasets,and the evaluation metrics increased by 1.9% on average and 5.2% at the highest.

关 键 词：深度学习 推荐算法 序列推荐 时间序列 多层感知机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]