Co-pairwise ranking model for item recommendation

Bin WU; Yun CHEN; Zhongchuan SUN; Yangdong YE

doi:10.11959/j.issn.1000-436x.2019137

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Co-pairwise ranking model for item recommendation

Correspondences | 更新时间：2024-06-05

- Co-pairwise ranking model for item recommendation
- Journal on Communications Vol. 40, Issue 9, Pages: 193-206(2019)
- 作者机构：
  
  郑州大学信息工程学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2018YFB1201403);The National Natural Science Foundation of China(61772475);The National Natural Science Foundation of China(61502434)
- DOI：10.11959/j.issn.1000-436x.2019137
  CLC： TP181
- Online First：2019-09，
  
  Published：25 September 2019
- 稿件说明：
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Bin WU, Yun CHEN, Zhongchuan SUN, et al. Co-pairwise ranking model for item recommendation[J]. Journal on Communications, 2019, 40(9): 193-206.
DOI：

Bin WU, Yun CHEN, Zhongchuan SUN, et al. Co-pairwise ranking model for item recommendation[J]. Journal on Communications, 2019, 40(9): 193-206. DOI： 10.11959/j.issn.1000-436x.2019137.

摘要

现有的推荐模型大多仅从用户角度进行建模，忽略了物品的功能关系对用户购买决策的影响。从用户和物品这2个角度，同时考虑用户-物品之间的交互关系和物品-物品之间的功能关系，提出了联合成对排序的推荐模型。考虑正样本的排名位置和负采样策略直接影响模型收敛速度，构建一种排序感知的学习算法，用于求解所提模型的参数。实验结果表明，与当前主流推荐算法相比，该算法在多个评价指标上具有明显的性能优势。

Abstract

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.

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