Integrating Triangle and Jaccard similarities for recommendation.
This paper proposes a new measure for recommendation through integrating Triangle and Jaccard similarities. The Triangle similarity considers both the length and the angle of rating vectors between them, while the Jaccard similarity considers non co-rating users. We compare the new similarity measur...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5560696?pdf=render |
| _version_ | 1818172154706919424 |
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| author | Shuang-Bo Sun Zhi-Heng Zhang Xin-Ling Dong Heng-Ru Zhang Tong-Jun Li Lin Zhang Fan Min |
| author_facet | Shuang-Bo Sun Zhi-Heng Zhang Xin-Ling Dong Heng-Ru Zhang Tong-Jun Li Lin Zhang Fan Min |
| author_sort | Shuang-Bo Sun |
| collection | DOAJ |
| description | This paper proposes a new measure for recommendation through integrating Triangle and Jaccard similarities. The Triangle similarity considers both the length and the angle of rating vectors between them, while the Jaccard similarity considers non co-rating users. We compare the new similarity measure with eight state-of-the-art ones on four popular datasets under the leave-one-out scenario. Results show that the new measure outperforms all the counterparts in terms of the mean absolute error and the root mean square error. |
| first_indexed | 2024-12-11T19:08:06Z |
| format | Article |
| id | doaj.art-b079005288af461f9d2ba7ac06d51788 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T19:08:06Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b079005288af461f9d2ba7ac06d517882022-12-22T00:53:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01128e018357010.1371/journal.pone.0183570Integrating Triangle and Jaccard similarities for recommendation.Shuang-Bo SunZhi-Heng ZhangXin-Ling DongHeng-Ru ZhangTong-Jun LiLin ZhangFan MinThis paper proposes a new measure for recommendation through integrating Triangle and Jaccard similarities. The Triangle similarity considers both the length and the angle of rating vectors between them, while the Jaccard similarity considers non co-rating users. We compare the new similarity measure with eight state-of-the-art ones on four popular datasets under the leave-one-out scenario. Results show that the new measure outperforms all the counterparts in terms of the mean absolute error and the root mean square error.http://europepmc.org/articles/PMC5560696?pdf=render |
| spellingShingle | Shuang-Bo Sun Zhi-Heng Zhang Xin-Ling Dong Heng-Ru Zhang Tong-Jun Li Lin Zhang Fan Min Integrating Triangle and Jaccard similarities for recommendation. PLoS ONE |
| title | Integrating Triangle and Jaccard similarities for recommendation. |
| title_full | Integrating Triangle and Jaccard similarities for recommendation. |
| title_fullStr | Integrating Triangle and Jaccard similarities for recommendation. |
| title_full_unstemmed | Integrating Triangle and Jaccard similarities for recommendation. |
| title_short | Integrating Triangle and Jaccard similarities for recommendation. |
| title_sort | integrating triangle and jaccard similarities for recommendation |
| url | http://europepmc.org/articles/PMC5560696?pdf=render |
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