计算机工程 ›› 2020, Vol. 46 ›› Issue (2): 96-102.doi: 10.19678/j.issn.1000-3428.0053379

• 先进计算与数据处理 • 上一篇    下一篇

基于需求预测的主动服务推荐方法

刘志中1, 张振兴1, 海燕2, 郭思慧1, 刘永利1   

  1. 1. 河南理工大学 计算机科学与技术学院, 河南 焦作 454002;
    2. 华北水利水电大学 信息工程学院, 郑州 450045
  • 收稿日期:2018-12-11 修回日期:2019-04-12 发布日期:2019-05-31
  • 作者简介:刘志中(1981-),男,副教授、博士,主研方向为服务计算、智能服务;张振兴,硕士研究生;海燕,教授、博士;郭思慧,硕士研究生;刘永利,副教授、博士。
  • 基金项目:
    国家自然科学基金(61872126,61772159)。

Active Service Recommendation Method Based on Requirement Prediction

LIU Zhizhong1, ZHANG Zhenxing1, HAI Yan2, GUO Sihui1, LIU Yongli1   

  1. 1. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454002, China;
    2. College of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
  • Received:2018-12-11 Revised:2019-04-12 Published:2019-05-31

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。

关键词: 需求预测, 主动服务, 服务推荐, 矩阵分解, 深度学习

Abstract: 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.

Key words: requirement prediction, active service, service recommendation, Matrix Factorization(MF), deep learning

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