Service recommendation method based on context-embedded support vector machine
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 context...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2019-09-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019190/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539356604497920 |
|---|---|
| author | Chenyang ZHAO Junling WANG |
| author_facet | Chenyang ZHAO Junling WANG |
| author_sort | Chenyang ZHAO |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-77a5b69d2b5d46c099bd9d4e99b38c75 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-77a5b69d2b5d46c099bd9d4e99b38c752025-01-14T07:17:42ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-09-0140617359729570Service recommendation method based on context-embedded support vector machineChenyang ZHAOJunling WANGCombined 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019190/service recommendationsupport vector machineembedded contextrating matrixrecommendation accuracy |
| spellingShingle | Chenyang ZHAO Junling WANG Service recommendation method based on context-embedded support vector machine Tongxin xuebao service recommendation support vector machine embedded context rating matrix recommendation accuracy |
| title | Service recommendation method based on context-embedded support vector machine |
| title_full | Service recommendation method based on context-embedded support vector machine |
| title_fullStr | Service recommendation method based on context-embedded support vector machine |
| title_full_unstemmed | Service recommendation method based on context-embedded support vector machine |
| title_short | Service recommendation method based on context-embedded support vector machine |
| title_sort | service recommendation method based on context embedded support vector machine |
| topic | service recommendation support vector machine embedded context rating matrix recommendation accuracy |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019190/ |
| work_keys_str_mv | AT chenyangzhao servicerecommendationmethodbasedoncontextembeddedsupportvectormachine AT junlingwang servicerecommendationmethodbasedoncontextembeddedsupportvectormachine |