An Enhanced Group Recommender System by Exploiting Preference Relation
With ties among people have been much more closer, making recommendations for groups of users became a more general demand, which facilitates the prevalence of group recommender system (GRS). Existing solutions for GRS are mostly established based on preference feedbacks of absolute form such as rat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8635454/ |
| _version_ | 1811273958915309568 |
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| author | Zhiwei Guo Wenru Zeng Heng Wang Yu Shen |
| author_facet | Zhiwei Guo Wenru Zeng Heng Wang Yu Shen |
| author_sort | Zhiwei Guo |
| collection | DOAJ |
| description | With ties among people have been much more closer, making recommendations for groups of users became a more general demand, which facilitates the prevalence of group recommender system (GRS). Existing solutions for GRS are mostly established based on preference feedbacks of absolute form such as ratings, yet neglecting that preference assessment criteria are usually heterogeneous among different members. In this paper, we propose GRS-PR, an enhanced group recommender system by exploiting preference relation. First, a preference relation-based multi-variate extreme learning machine model is formulated to predict unknown preference relations in candidate items. Second, on the basis of predicted results, borda voting rule is employed to generate recommendation results from candidate items. In addition, efficiency, parameter sensitivity, and sparsity tolerance of the GRS-PR are evaluated through a set of experiments. |
| first_indexed | 2024-04-12T23:10:08Z |
| format | Article |
| id | doaj.art-23620c2f8b5541aa8a56bd1b242fff7e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:10:08Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-23620c2f8b5541aa8a56bd1b242fff7e2022-12-22T03:12:49ZengIEEEIEEE Access2169-35362019-01-017248522486410.1109/ACCESS.2019.28977608635454An Enhanced Group Recommender System by Exploiting Preference RelationZhiwei Guo0https://orcid.org/0000-0001-8868-6913Wenru Zeng1Heng Wang2Yu Shen3Chongqing Engineering Laboratory for Detection, Control and Integrated System, Chongqing Technology and Business University, Chongqing, ChinaNational Base of International Science and Technology Cooperation for Intelligent Manufacturing Service,Chongqing Technology and Business University, Chongqing, ChinaCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Henan, ChinaNational Base of International Science and Technology Cooperation for Intelligent Manufacturing Service,Chongqing Technology and Business University, Chongqing, ChinaWith ties among people have been much more closer, making recommendations for groups of users became a more general demand, which facilitates the prevalence of group recommender system (GRS). Existing solutions for GRS are mostly established based on preference feedbacks of absolute form such as ratings, yet neglecting that preference assessment criteria are usually heterogeneous among different members. In this paper, we propose GRS-PR, an enhanced group recommender system by exploiting preference relation. First, a preference relation-based multi-variate extreme learning machine model is formulated to predict unknown preference relations in candidate items. Second, on the basis of predicted results, borda voting rule is employed to generate recommendation results from candidate items. In addition, efficiency, parameter sensitivity, and sparsity tolerance of the GRS-PR are evaluated through a set of experiments.https://ieeexplore.ieee.org/document/8635454/Group recommender systempreference assessment criteriapreference relationextreme learning machineborda voting rule |
| spellingShingle | Zhiwei Guo Wenru Zeng Heng Wang Yu Shen An Enhanced Group Recommender System by Exploiting Preference Relation IEEE Access Group recommender system preference assessment criteria preference relation extreme learning machine borda voting rule |
| title | An Enhanced Group Recommender System by Exploiting Preference Relation |
| title_full | An Enhanced Group Recommender System by Exploiting Preference Relation |
| title_fullStr | An Enhanced Group Recommender System by Exploiting Preference Relation |
| title_full_unstemmed | An Enhanced Group Recommender System by Exploiting Preference Relation |
| title_short | An Enhanced Group Recommender System by Exploiting Preference Relation |
| title_sort | enhanced group recommender system by exploiting preference relation |
| topic | Group recommender system preference assessment criteria preference relation extreme learning machine borda voting rule |
| url | https://ieeexplore.ieee.org/document/8635454/ |
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