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