Co-pairwise ranking model for item recommendation

被引：0
|
作者：
Wu B. [1 ]
Chen Y. [1 ]
Sun Z. [1 ]
Ye Y. [1 ]
机构：
[1] School of Information Engineering, Zhengzhou University, Zhengzhou
来源：
Tongxin Xuebao/Journal on Communications | 2019年 / 40卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Collaborative filtering; Implicit feedback; Item recommendation; Matrix factorization; Pairwise ranking;
D O I：
10.11959/j.issn.1000-436x.2019137
中图分类号：
学科分类号：
摘要：
Most of existing recommendation models constructed pairwise samples only from a user's perspective. Nevertheless, they overlooked the functional relationships among items--A key factor that could significantly influence user purchase decision-making process. To this end, a co-pairwise ranking model was proposed, which modeled a user's preference for a given item as the combination of user-item interactions and item-item complementarity relationships. Considering that the rank position of positive sample and the negative sampler had a direct impact on the rate of convergence, a rank-aware learning algorithm was devised for optimizing the proposed model. Extensive experiments on four real-word datasets are conducted to evaluate of the proposed model. The experimental results demonstrate that the devised algorithm significantly outperforms a series of state-of-the-art recommendation algorithms in terms of multiple evaluation metrics. © 2019, Editorial Board of Journal on Communications. All right reserved.
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页码：193 / 207
页数：14
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