Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence

作     者：Xuan Yang James A.Esquivel Xuan Yang;James A.Esquivel

作者机构：Graduate SchoolAngeles University FoundationAngeles City 2009Philippines Shandong Provincial University Laboratory for Protected HorticultureWeifang University of Science and TechnologyWeifang 262700China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2024年第29卷第1期

页      面：185-196页

核心收录：

学科分类：0808[工学-电气工程] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：personalized recommendations evolving interests embedding LsTM networks 

摘      要：Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business ***,deep neural network-based sequential recommendation models gained considerable ***,existing approaches pay litle attention to users dynamically evolving interests,which are influenced by product attributes,especially product *** overcome these challenges,we propose a dynamic personalized recommendation model:***,we first embed product information and attribute information into a unified data ***,we exploit long short-term memory(LsTM)networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM ***,similarity values between users are measured through pairwise interest features,and personalized recommendation lists are generated.A series of experiments reveal the superiority of the proposed method compared withotheradvanced methods.