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

Abstract

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

CLC Number:

TP393

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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Service recommendation method based on context-embedded support vector machine

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