联合成对排序的物品推荐模型

吴宾; 陈允; 孙中川; 叶阳东

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

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联合成对排序的物品推荐模型

学术通信 | 更新时间：2024-06-05

- 联合成对排序的物品推荐模型
- Co-pairwise ranking model for item recommendation
- 通信学报 2019年40卷第9期页码：193-206
- 作者机构：
  
  郑州大学信息工程学院，河南郑州 450001
- 作者简介：
  
  [ "吴宾（1991- ），男，河南柘城人，郑州大学博士生，主要研究方向为推荐系统、社交网络及多媒体。" ]
  [ "陈允（1990- ），女，河南虞城人，郑州大学硕士生，主要研究方向为推荐系统和社交网络。" ]
  [ "孙中川（1992- ），男，河南原阳人，郑州大学硕士生，主要研究方向为推荐系统和对抗网络。" ]
  [ "叶阳东（1962- ），男，河南潢川人，博士，郑州大学教授、博士生导师，主要研究方向为机器学习、智能系统、数据库等。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB1201403);国家自然科学基金资助项目(61772475);国家自然科学基金资助项目(61502434)
- DOI：10.11959/j.issn.1000-436x.2019137
  中图分类号： TP181
- 网络出版日期：2019-09，
  
  纸质出版日期：2019-09-25
- 稿件说明：
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吴宾, 陈允, 孙中川, 等. 联合成对排序的物品推荐模型[J]. 通信学报, 2019,40(9):193-206.

Bin WU, Yun CHEN, Zhongchuan SUN, et al. Co-pairwise ranking model for item recommendation[J]. Journal on communications, 2019, 40(9): 193-206.
吴宾, 陈允, 孙中川, 等. 联合成对排序的物品推荐模型[J]. 通信学报, 2019,40(9):193-206. DOI： 10.11959/j.issn.1000-436x.2019137.

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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references

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