Collaborative filtering recommendation algorithm based on rough set rule extraction
To address the problem that in a practical recommendation system (RS),because of the datasets are often very sparse,the traditional collaborative filtering (CF) approach cannot provide recommendations with higher quality,a novel CF based on rough set rule extraction was proposed.Firstly,the attribut...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-01-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020028/ |
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| author | Yonggong REN Yunpeng ZHANG Zhipeng ZHANG |
| author_facet | Yonggong REN Yunpeng ZHANG Zhipeng ZHANG |
| author_sort | Yonggong REN |
| collection | DOAJ |
| description | To address the problem that in a practical recommendation system (RS),because of the datasets are often very sparse,the traditional collaborative filtering (CF) approach cannot provide recommendations with higher quality,a novel CF based on rough set rule extraction was proposed.Firstly,the attributes of user/item and the user-item rating matrix were used to construct a decision table.Then,the core value of each rule in the table was extracted through using the decision table reduction algorithm.Finally,according to the nuclear value decision rule of the core value table,the reductions of all decision rules were utilized to predict the rating scores of un-rated items.Experimental results suggest that the proposed approach can alleviate the data sparsity problem of CF,and provide recommendations with higher accuracy. |
| format | Article |
| id | doaj-art-39bbb1ae4c714821b524ab7153575950 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-39bbb1ae4c714821b524ab71535759502025-01-14T07:18:23ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-01-0141768359732544Collaborative filtering recommendation algorithm based on rough set rule extractionYonggong RENYunpeng ZHANGZhipeng ZHANGTo address the problem that in a practical recommendation system (RS),because of the datasets are often very sparse,the traditional collaborative filtering (CF) approach cannot provide recommendations with higher quality,a novel CF based on rough set rule extraction was proposed.Firstly,the attributes of user/item and the user-item rating matrix were used to construct a decision table.Then,the core value of each rule in the table was extracted through using the decision table reduction algorithm.Finally,according to the nuclear value decision rule of the core value table,the reductions of all decision rules were utilized to predict the rating scores of un-rated items.Experimental results suggest that the proposed approach can alleviate the data sparsity problem of CF,and provide recommendations with higher accuracy.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020028/personalized recommendationcollaborative filteringrough setrule extraction |
| spellingShingle | Yonggong REN Yunpeng ZHANG Zhipeng ZHANG Collaborative filtering recommendation algorithm based on rough set rule extraction Tongxin xuebao personalized recommendation collaborative filtering rough set rule extraction |
| title | Collaborative filtering recommendation algorithm based on rough set rule extraction |
| title_full | Collaborative filtering recommendation algorithm based on rough set rule extraction |
| title_fullStr | Collaborative filtering recommendation algorithm based on rough set rule extraction |
| title_full_unstemmed | Collaborative filtering recommendation algorithm based on rough set rule extraction |
| title_short | Collaborative filtering recommendation algorithm based on rough set rule extraction |
| title_sort | collaborative filtering recommendation algorithm based on rough set rule extraction |
| topic | personalized recommendation collaborative filtering rough set rule extraction |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020028/ |
| work_keys_str_mv | AT yonggongren collaborativefilteringrecommendationalgorithmbasedonroughsetruleextraction AT yunpengzhang collaborativefilteringrecommendationalgorithmbasedonroughsetruleextraction AT zhipengzhang collaborativefilteringrecommendationalgorithmbasedonroughsetruleextraction |