摘要
在智能计算领域,网络中可用服务数量与类型的快速增长,使用户更依赖于服务完成各种业务,然而当前“请求-响应”被动式的服务模式严重影响了用户体验与资源利用率。为智能感知用户需求并主动为用户推荐合适的服务,通过引入需求预测过程,提出一种主动服务推荐方法。利用矩阵分解算法从大量历史服务使用数据中提取用户特征和服务特征,据此训练深度学习模型并预测用户的服务需求,进而为用户推荐其所需要的服务。基于真实数据的实验结果表明,该方法较单一的矩阵分解模型和深度神经网络模型具有更高的服务推荐准确性和稳定性。
In the intelligent computing field,the rapid growth of available Internet services makes users increasingly dependent on services to complete various businesses,but the passive“request-response”service model seriously decreases user experience and resource utilization.To intelligently perceive user requirements and proactively recommend appropriate services to users,this paper proposes a method of active service recommendation based on user requirement prediction.The method firstly extracts user features and service features from massive data of historical services by using matrix factorization.On this basis,the extracted data is used to train the deep learning model and predict service demands of users,so as to recommend appropriate services to users.Experimental results on real data show that the proposed method has higher accuracy and stability of service recommendation than simply a matrix factorization model or deep neural network model.
作者
刘志中
张振兴
海燕
郭思慧
刘永利
LIU Zhizhong;ZHANG Zhenxing;HAI Yan;GUO Sihui;LIU Yongli(College of Computer Science and Technology,Henan Polytechnic University,Jiaozuo,Henan 454002,China;College of Information Engineering,North China University of Water Resources and Electric Power,Zhengzhou 450045,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第2期96-102,共7页
Computer Engineering
基金
国家自然科学基金(61872126,61772159)
关键词
需求预测
主动服务
服务推荐
矩阵分解
深度学习
requirement prediction
active service
service recommendation
Matrix Factorization(MF)
deep learning
