User similarity-based collaborative filtering recommendation algorithm
Collaborative filtering recommendation algorithms widely used in e-commerce, recommend interesting content for users from massive data resources by studying their preferences and interests. The focus of similarity and evaluation have been changed when applied to social networks, however, they cause...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2014-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.02.003/ |
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| _version_ | 1841539719047938048 |
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| author | Hui-gui RONG Sheng-xu HUO Chun-hua HU Jin-xia MO |
| author_facet | Hui-gui RONG Sheng-xu HUO Chun-hua HU Jin-xia MO |
| author_sort | Hui-gui RONG |
| collection | DOAJ |
| description | Collaborative filtering recommendation algorithms widely used in e-commerce, recommend interesting content for users from massive data resources by studying their preferences and interests. The focus of similarity and evaluation have been changed when applied to social networks, however, they cause low efficiency and accuracy of the recommen-dation algorithms. User similarity was introduced for redefining the attribute similarity and similarity composition as well as the method of similarity calculating, then a new collaborative filtering recommendation algorithm based on user attrib-utes was designed and some methods for user satisfaction and quality of recommendations were presented. The experi-mental result shows that the new algorithm can effectively improve the accuracy, quality and user satisfaction of recom-mendation system in social networks. |
| format | Article |
| id | doaj-art-331b8796c35342af93eba1a5b4d4ae96 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-331b8796c35342af93eba1a5b4d4ae962025-01-14T06:42:37ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-02-0135162459679271User similarity-based collaborative filtering recommendation algorithmHui-gui RONGSheng-xu HUOChun-hua HUJin-xia MOCollaborative filtering recommendation algorithms widely used in e-commerce, recommend interesting content for users from massive data resources by studying their preferences and interests. The focus of similarity and evaluation have been changed when applied to social networks, however, they cause low efficiency and accuracy of the recommen-dation algorithms. User similarity was introduced for redefining the attribute similarity and similarity composition as well as the method of similarity calculating, then a new collaborative filtering recommendation algorithm based on user attrib-utes was designed and some methods for user satisfaction and quality of recommendations were presented. The experi-mental result shows that the new algorithm can effectively improve the accuracy, quality and user satisfaction of recom-mendation system in social networks.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.02.003/collaborative filteringuser similarityattribute similarityinteractive similarityuser satisfaction |
| spellingShingle | Hui-gui RONG Sheng-xu HUO Chun-hua HU Jin-xia MO User similarity-based collaborative filtering recommendation algorithm Tongxin xuebao collaborative filtering user similarity attribute similarity interactive similarity user satisfaction |
| title | User similarity-based collaborative filtering recommendation algorithm |
| title_full | User similarity-based collaborative filtering recommendation algorithm |
| title_fullStr | User similarity-based collaborative filtering recommendation algorithm |
| title_full_unstemmed | User similarity-based collaborative filtering recommendation algorithm |
| title_short | User similarity-based collaborative filtering recommendation algorithm |
| title_sort | user similarity based collaborative filtering recommendation algorithm |
| topic | collaborative filtering user similarity attribute similarity interactive similarity user satisfaction |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.02.003/ |
| work_keys_str_mv | AT huiguirong usersimilaritybasedcollaborativefilteringrecommendationalgorithm AT shengxuhuo usersimilaritybasedcollaborativefilteringrecommendationalgorithm AT chunhuahu usersimilaritybasedcollaborativefilteringrecommendationalgorithm AT jinxiamo usersimilaritybasedcollaborativefilteringrecommendationalgorithm |