计算机与数字工程2019，Vol.47Issue(7)：1694-1701,1733,9.DOI:10.3969/j.issn.1672-9722.2019.07.027
基于张量分解的跨领域推荐方法
Cross-domain Recommendation System Based on Tensor Decomposition
摘要
Abstract
In the context of mass data and cloud computing,the traditional single-domain recommendation algorithm is diffi?cult to adapt to cross-domain information recommendation service. Collaborative filtering is a simple and common recommendation algorithm,but when the target domain is very sparse,the performance of serious degradation,with the target domain associated with the field of auxiliary domain for cross-domain recommendations is an effective way to solve this problem. Most of the existing cross-domain recommendation models are based on two-dimensional rating matrix,and many other dimension information is lost, leading to degraded performance. In this paper,a cross-domain recommendation method based on tensor decomposition is pro?posed,which can reduce the sparseness of data and improve the diversity and accuracy of the proposed results by extracting the learning model in different fields. Many experiments on three publicly presented real data sets show that the model＇s recommendation accuracy is superior to some of the most advanced recommendation models,it can be applied to large-scale information recommen?dation service.关键词
推荐系统/协同滤波/跨领域/HOSVD分解Key words
recommend system/collaborative filter/cross-domain/HOSVD decomposition分类
信息技术与安全科学引用本文复制引用
孙华成,王永利,赵亮,陈广生..基于张量分解的跨领域推荐方法[J].计算机与数字工程,2019,47(7):1694-1701,1733,9.基金项目
国家自然科学基金项目(编号:61170035,61502233) （编号:61170035,61502233）
江苏省科技成果转化专项资金项目(编号:BA2013047) （编号:BA2013047）
江苏省六大人才高峰项目(编号:WLW-004) （编号:WLW-004）
兵科院预研项目(编号:62201070151) （编号:62201070151）
中央高校基本科研业务费专项资金项目(编号:30916011328)资助. （编号:30916011328）
