Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation
Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/248084 |
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| _version_ | 1832564504514265088 |
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| author | Jinyu Hu Zhiwei Gao Weisen Pan |
| author_facet | Jinyu Hu Zhiwei Gao Weisen Pan |
| author_sort | Jinyu Hu |
| collection | DOAJ |
| description | Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels. |
| format | Article |
| id | doaj-art-cb32b6e4e5c04616b31c3221e424f174 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-cb32b6e4e5c04616b31c3221e424f1742025-02-03T01:10:51ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/248084248084Multiangle Social Network Recommendation Algorithms and Similarity Network EvaluationJinyu Hu0Zhiwei Gao1Weisen Pan2Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USAFaculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, NE1 8ST, UKDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USAMultiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.http://dx.doi.org/10.1155/2013/248084 |
| spellingShingle | Jinyu Hu Zhiwei Gao Weisen Pan Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation Journal of Applied Mathematics |
| title | Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation |
| title_full | Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation |
| title_fullStr | Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation |
| title_full_unstemmed | Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation |
| title_short | Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation |
| title_sort | multiangle social network recommendation algorithms and similarity network evaluation |
| url | http://dx.doi.org/10.1155/2013/248084 |
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