SIA OpenIR  > 数字工厂研究室
Alternative TitleFactorization Machines Based on Pairwise Learning with Implicit Feedback
靳冠坤1,2; 库涛1; 温广波3; 贾敬崧1,2
Source Publication科学技术与工程
Contribution Rank1
Funding Organization国家重点研发计划(2017YFB030640X)资助
Keyword隐式反馈 上下文 个性化推荐 排序学习 因子分解机
Other AbstractIn view of the problem of personalized recommendation for implicit feedback and how to incorporate users' contextual information in recommendation,a recommendation model combined with pairwise learning and factorization machine was proposed. First of all,the method of pairwise learning was used 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. Specifically,the user's preference was modeled from the perspective of optimizing the ranking of items,eventually,provided personalized recommendations for different users according to the model score. Experiments also show that the model proposed is better than the other three contrast algorithms in terms of ranking indicators such as MAP and NDCG. While addressing the recommended problems under implicit feedback,the proposed model can further improve the accuracy of recommendation by using the user's contextual information.
Document Type期刊论文
Corresponding Author库涛
Recommended Citation
GB/T 7714
靳冠坤,库涛,温广波,等. 隐式反馈场景下基于Pairwise排序学习的因子分解机算法[J]. 科学技术与工程,2018,18(16):217-222.
APA 靳冠坤,库涛,温广波,&贾敬崧.(2018).隐式反馈场景下基于Pairwise排序学习的因子分解机算法.科学技术与工程,18(16),217-222.
MLA 靳冠坤,et al."隐式反馈场景下基于Pairwise排序学习的因子分解机算法".科学技术与工程 18.16(2018):217-222.
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