Service recommendation method based on context-embedded support vector machine

Chenyang ZHAO; Junling WANG

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

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Service recommendation method based on context-embedded support vector machine

Papers | 更新时间：2024-06-05

- Service recommendation method based on context-embedded support vector machine
- Journal on Communications Vol. 40, Issue 9, Pages: 61-73(2019)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  The Natural Science Project of Henan Science and Technology Department(182102210388);The Program for Innovative Research Team in University of Henan Province(17IRTSTHN011)
- DOI：10.11959/j.issn.1000-436x.2019190
  CLC： TP393
- Online First：2019-09，
  
  Published：25 September 2019
- 稿件说明：
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Chenyang ZHAO, Junling WANG. Service recommendation method based on context-embedded support vector machine[J]. Journal on Communications, 2019, 40(9): 61-73.
DOI：

Chenyang ZHAO, Junling WANG. Service recommendation method based on context-embedded support vector machine[J]. Journal on Communications, 2019, 40(9): 61-73. DOI： 10.11959/j.issn.1000-436x.2019190.

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。

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.

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Keywords

references

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