Pairwise Factorization Machines for Personalized Ranking with Implicit Feedback
文献类型：会议论文
| 作者 | Ku T(库涛) ; Jin GK(靳冠坤)
|
| 出版日期 | 2018 |
| 会议日期 | July 19-23, 2018 |
| 会议地点 | Tianjin, China |
| 页码 | 1340-1344 |
| 英文摘要 | Pairwise learning is a novel technique for personalized recommendation with implicit feedback. Pairwise learning algorithms can well learn user's preference,from not only the observed user feedbacks but also the underlying interactions between users and items. It also has long proved that incorporate contextual information in model can further improve the accuracy of recommendation. In terms of the problem of personalized recommendation for implicit feedback and how to incorporate users' contextual information in recommendation, this paper proposes a recommendation model combined with pairwise learning and factorization machine. First of all, we use the method of pairwise learning to solve the problem of negative feedback missing under implicit feedback scenario, and then choose the factorization machine as ranking function to model the user's contextual information, and provide personalized recommendations for different users according to the model score. Experiments also show that the model proposed in this paper is better than the other three contrast algorithms in terms of ranking metrics such as MAP and NDCG. |
| 源文献作者 | IEEE Robotics & Automation Society |
| 产权排序 | 1 |
| 会议录 | Proceedings of 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2379-7711 |
| ISBN号 | 978-1-5386-7056-9 |
| WOS记录号 | WOS:000468941800236 |
| 源URL | [http://ir.sia.cn/handle/173321/24683]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 通讯作者 | Ku T(库涛) |
| 作者单位 | Chinese Academy of Sciences, Digital Factory Department Shenyang Institute of Automation, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Ku T,Jin GK. Pairwise Factorization Machines for Personalized Ranking with Implicit Feedback[C]. 见:. Tianjin, China. July 19-23, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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