Joint recommendation algorithm based on tensor completion and user preference
Aiming at the problem that existing recommendation algorithms have little regard for user preference,and the recommendation result is not satisfactory,a joint recommendation algorithm based on tensor completion and user preference was proposed.First,a user-item-category 3-dimensional tensor was buil...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-12-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2019231 |
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| _version_ | 1850211467464278016 |
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| author | Zhi XIONG Kai XU Lingru CAI Weihong CAI |
| author_facet | Zhi XIONG Kai XU Lingru CAI Weihong CAI |
| author_sort | Zhi XIONG |
| collection | DOAJ |
| description | Aiming at the problem that existing recommendation algorithms have little regard for user preference,and the recommendation result is not satisfactory,a joint recommendation algorithm based on tensor completion and user preference was proposed.First,a user-item-category 3-dimensional tensor was built based on user-item scoring matrix and item-category matrix.Then,the Frank-Wolfe algorithm was used for iterative calculation to fill in the missing data of the tensor.At the same time,a user category preference matrix and a scoring preference matrix were built based on the 3-dimensional tensor.Finally,a joint recommendation algorithm was designed based on the completed tensor and the two preference matrices,and the differential evolution algorithm was used for parameter tuning.The experimental results show that compared with some typical and newly proposed recommendation algorithms,the proposed algorithm is superior to the compare algorithms,the precision is improved by 1.96% ~ 3.44% on average,and the recall rate is improved by 1.35%~2.40% on average. |
| format | Article |
| id | doaj-art-55d5ad8dabed438fbab7d9147ad1217d |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-55d5ad8dabed438fbab7d9147ad1217d2025-08-20T02:09:34ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-12-014015516659731138Joint recommendation algorithm based on tensor completion and user preferenceZhi XIONGKai XULingru CAIWeihong CAIAiming at the problem that existing recommendation algorithms have little regard for user preference,and the recommendation result is not satisfactory,a joint recommendation algorithm based on tensor completion and user preference was proposed.First,a user-item-category 3-dimensional tensor was built based on user-item scoring matrix and item-category matrix.Then,the Frank-Wolfe algorithm was used for iterative calculation to fill in the missing data of the tensor.At the same time,a user category preference matrix and a scoring preference matrix were built based on the 3-dimensional tensor.Finally,a joint recommendation algorithm was designed based on the completed tensor and the two preference matrices,and the differential evolution algorithm was used for parameter tuning.The experimental results show that compared with some typical and newly proposed recommendation algorithms,the proposed algorithm is superior to the compare algorithms,the precision is improved by 1.96% ~ 3.44% on average,and the recall rate is improved by 1.35%~2.40% on average.http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2019231recommendation algorithm;joint recommendation;tensor completion;user preference |
| spellingShingle | Zhi XIONG Kai XU Lingru CAI Weihong CAI Joint recommendation algorithm based on tensor completion and user preference Tongxin xuebao recommendation algorithm;joint recommendation;tensor completion;user preference |
| title | Joint recommendation algorithm based on tensor completion and user preference |
| title_full | Joint recommendation algorithm based on tensor completion and user preference |
| title_fullStr | Joint recommendation algorithm based on tensor completion and user preference |
| title_full_unstemmed | Joint recommendation algorithm based on tensor completion and user preference |
| title_short | Joint recommendation algorithm based on tensor completion and user preference |
| title_sort | joint recommendation algorithm based on tensor completion and user preference |
| topic | recommendation algorithm;joint recommendation;tensor completion;user preference |
| url | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2019231 |
| work_keys_str_mv | AT zhixiong jointrecommendationalgorithmbasedontensorcompletionanduserpreference AT kaixu jointrecommendationalgorithmbasedontensorcompletionanduserpreference AT lingrucai jointrecommendationalgorithmbasedontensorcompletionanduserpreference AT weihongcai jointrecommendationalgorithmbasedontensorcompletionanduserpreference |