基于隐含上下文支持向量机的服务推荐方法

doi:10.11959/j.issn.1000-436x.2019190

通信学报 ›› 2019, Vol. 40 ›› Issue (9): 61-73.doi: 10.11959/j.issn.1000-436x.2019190

基于隐含上下文支持向量机的服务推荐方法

赵晨阳¹,王俊岭²

¹ 河南工业大学信息科学与工程学院，河南郑州450001
² 河南工业大学理学院，河南郑州450001

修回日期:2019-07-07 出版日期:2019-09-25 发布日期:2019-09-28
作者简介:赵晨阳（1982- ），男，河南郸城人，博士，河南工业大学讲师，主要研究方向为人工智能、深度学习、智能推荐等。|王俊岭（1983- ），男，河南郑州人，博士，河南工业大学讲师，主要研究方向为人工智能、组合优化、智能推荐等。
基金资助:
河南省科技厅自然科学基金资助项目(182102210388);河南省高校科技创新团队支持计划基金资助项目(17IRTSTHN011)

Service recommendation method based on context-embedded support vector machine

Chenyang ZHAO¹,Junling WANG²

¹ College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China
² College of Science,Henan University of Technology,Zhengzhou 450001,China

Revised:2019-07-07 Online:2019-09-25 Published:2019-09-28
Supported by:
The Natural Science Project of Henan Science and Technology Department(182102210388);The Program for Innovative Research Team in University of Henan Province(17IRTSTHN011)

摘要/Abstract

摘要：

结合上下文信息和支持向量机（SVM），提出了一种基于隐含上下文支持向量机的服务推荐方法。首先，根据用户所处的不同上下文信息对用户评分矩阵进行修正，使其带有隐含的上下文信息；其次，将带有隐含上下文信息的服务评分向量作为服务的特征向量，构建训练集，上下文信息的引入并没有增加服务特征向量的维数；然后，根据训练集使用SVM获得目标用户的分类超平面，构建SVM预测模型；最后，计算目标用户未使用服务的特征向量点与超平面的距离，综合考虑该距离以及相似用户的推荐，做出服务推荐。实验结果表明，所提推荐方法在不同的评分矩阵密度下均具有较好的推荐精度，并且能够缩短推荐时间。

关键词: 服务推荐, 支持向量机, 隐含上下文, 评分矩阵, 推荐精度

Abstract:

Combined with contexts and SVM,a service recommendation method based on context-embedded support vector machine (SRM-CESVM) was proposed.Firstly,according to the different contexts,the user rating matrix was modified to make it with embedded contexts.Secondly,the rating vectors with embedded contexts were used as service feature vectors to construct training set,meanwhile the dimension of service feature vector were not increased by the introduction of contexts.Thirdly,a separation hyperplane for active user was acquired based on training set using SVM,and then the SVM prediction model was built.Finally,the distances between the feature vector points representing the active users' unused services and the hyperplane were calculated.Considering the distances and the recommendation of similar users,the service list was recommended.The experimental results further demonstrate that the proposed method has better recommendation accuracy under different rating matrix densities and can reduce recommendation time.

Key words: service recommendation, support vector machine, embedded context, rating matrix, recommendation accuracy

中图分类号:

TP393

赵晨阳,王俊岭. 基于隐含上下文支持向量机的服务推荐方法[J]. 通信学报, 2019, 40(9): 61-73.

Chenyang ZHAO,Junling WANG. Service recommendation method based on context-embedded support vector machine[J]. Journal on Communications, 2019, 40(9): 61-73.

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基于隐含上下文支持向量机的服务推荐方法

Service recommendation method based on context-embedded support vector machine

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