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| personalized recommendation using implicit interaction information | |
| Nancheng Liu; Jiang Qingshan; Chen HaiShan; Wang Beizhan | |
| 2011 | |
| Conference Name | 6th International Conference on Computer Science and Education, ICCSE 2011 |
| Source | ICCSE 2011 - 6th International Conference on Computer Science and Education, Final Program and Proceedings |
| Pages | 1340-1345 |
| Conference Date | August 3, 2011 - August 5, 2011 |
| Conference Place | Singapore, Singapore |
| Indexed Type | EI |
| ISBN | 9781424497188 |
| Department | (1) Software School Xiamen University Xiamen China; (2) Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China |
| English Abstract | Currently, the information in the internet is becoming explosive. In order to help the users searching the items they are interested in, such as, the news, the books, in this paper, we propose an automatic personalized recommendation algorithm by constructing the social graph resting on the users' implicit interaction information. We at first introduce a metric to measure the users' affinity based on their implicit interaction information to construct a social graph, and then categorize the users into different clusters within which they will have similar tastes, finally, we use a personalized recommendation algorithm to recommend the items shared in the same cluster to the users. The experiments on a book data set are performed to demonstrate that our proposed method can well generate the recommendations which users will be interested in with high accuracy and efficiency. © 2011 IEEE.; Currently, the information in the internet is becoming explosive. In order to help the users searching the items they are interested in, such as, the news, the books, in this paper, we propose an automatic personalized recommendation algorithm by constructing the social graph resting on the users' implicit interaction information. We at first introduce a metric to measure the users' affinity based on their implicit interaction information to construct a social graph, and then categorize the users into different clusters within which they will have similar tastes, finally, we use a personalized recommendation algorithm to recommend the items shared in the same cluster to the users. The experiments on a book data set are performed to demonstrate that our proposed method can well generate the recommendations which users will be interested in with high accuracy and efficiency. © 2011 IEEE. |
| Keyword | Algorithms Computer Science Education Computing |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16308 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Nancheng Liu,Jiang Qingshan,Chen HaiShan,et al. personalized recommendation using implicit interaction information[C],2011:1340-1345. |
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