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

您当前的位置：

首页 >

文章列表页 >

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
- 网络首发：2019-09，
  
  纸质出版：2019-09-25
- 稿件说明：
移动端阅览
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.

关键词

Keywords

references

BOBADILLA J , ORTEGA F , HERNANDO A , et al . Recommender systems survey [J ] . Knowledge-Based Systems , 2013 ( 46 ): 109 - 132 .

LIU Q , CHEN E , XIONG H , et al . Enhancing collaborative filtering by user interest expansion via personalized ranking [J ] . IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics) , 2012 , 42 ( 1 ): 218 - 233 .

何明 , 刘伟世 , 张江 . 支持推荐非空率的关联规则推荐算法 [J ] . 通信学报 , 2017 , 38 ( 10 ): 18 - 25 .

HE M , LIU S W , ZHANG J . Association rules recommendation algo-rithm supporting recommendation nonempty [J ] . Journal on Commu-nications , 2017 , 38 ( 10 ): 18 - 25 .

SMITH B , LINDEN G . Two decades of recommender systems at amazoncom [J ] . IEEE Internet Computing , 2017 , 21 ( 3 ): 12 - 18 .

吴宾 , 娄铮铮 , 叶阳东 . 联合正则化的矩阵分解推荐算法 [J ] . 软件学报 , 2018 , 29 ( 9 ): 2681 - 2696 .

WU B , LOU Z Z , YE Y D . Co-regularized matrix factorization rec-ommendation algorithm [J ] . Journal of Software , 2018 , 29 ( 9 ): 2681 - 2696 .

ADOMAVICIUS G , TUZHILIN A . Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions [J ] . IEEE Transactions on Knowledge and Data Engineering , 2005 , 17 ( 16 ): 734 - 749 .

PAN W , CHEN L . GBPR:group preference based Bayesian personalized ranking for one-class collaborative filtering [C ] // The International Joint Conference on Artificial Intelligence . AAAI Press , 2013 : 2691 - 2697 .

HU Y , KOREN Y , VOLINSKY C . Collaborative filtering for implicit feedback datasets [C ] // The Eighth IEEE International Conference on Data Mining . IEEE , 2008 : 263 - 272 .

HE X , ZHANG H , KAN M Y , et al . Fast matrix factorization for online recommendation with implicit feedback [C ] // The International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM , 2016 : 549 - 558 .

RENDLE S , FREUDENTHALER C , GANTNER Z , et al . BPR:Bayesian personalized ranking from implicit feedback [C ] // The Conference on Uncertainty in Artificial Intelligence . AUAI Press , 2009 : 452 - 461 .

DING J , FENG F , HE X , et al . An improved sampler for Bayesian personalized ranking by leveraging view data [C ] // Companion Proceedings of the Web Conference 2018 . ACM , 2018 : 13 - 14 .

SUN Z , WU B , WU Y , et al . APL:adversarial pairwise learning for recommender systems [J ] . Expert Systems with Applications , 2019 ( 118 ): 573 - 584 .

ZHAO T , MCAULEY J , KING I . Leveraging social connections to improve personalized ranking for collaborative filtering [C ] // The ACM International Conference on Conference on Information and Knowledge Management . ACM , 2014 : 261 - 270 .

LERCHE L , JANNACH D . Using graded implicit feedback for Bayesian personalized ranking [C ] // The ACM Conference on Recommender Systems . ACM , 2014 : 353 - 356 .

PARK Y J , TUZHILIN A . The long tail of recommender systems and how to leverage it [C ] // The 2008 ACM Conference on Recommender Systems . ACM , 2008 : 11 - 18 .

RENDLE S , FREUDENTHALER C . Improving pairwise learning for item recommendation from implicit feedback [C ] // The ACM International Conference on Web Search and Data Mining . ACM , 2014 : 273 - 282 .

HE X , LIAO L , ZHANG H , et al . Neural collaborative filtering [C ] // The International Conference on World Wide Web . ACM , 2017 : 173 - 182 .

CHEN X , WANG P , QIN Z , et al . HLBPR:a hybrid local Bayesian personal ranking method [C ] // The International Conference Companion on World Wide Web . ACM , 2016 : 21 - 22 .

MCAULEY J , PANDEY R , LESKOVEC J . Inferring networks of substitutable and complementary products [C ] // The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM , 2015 : 785 - 794 .

WANG Z , JIANG Z , REN Z , et al . A path-constrained framework for discriminating substitutable and complementary products in e-commerce [C ] // The ACM International Conference on Web Search and Data Mining . ACM , 2018 : 619 - 627 .

OTUNBA R , RUFAI R A , LIN J . MPR:multi-objective pairwise ranking [C ] // The ACM Conference on Recommender Systems . ACM , 2017 : 170 - 178 .

WESTON J , BENGIO S , USUNIER N . WSABIE:scaling up to large vocabulary image annotation [C ] // The International Joint Conference on Artificial Intelligence . AAAI Press , 2011 : 2764 - 2770 .

HE R , MCAULEY J . VBPR:visual Bayesian personalized ranking from implicit feedback [C ] // The AAAI Conference on Artificial Intelligence . AAAI Press , 2016 : 144 - 150 .

WU B , YE Y , CHEN Y . Visual appearance or functional complementarity:which aspect affects your decision making? [J ] . Information Sciences , 2019 ( 476 ): 19 - 37 .

彭舰 , 王屯屯 , 陈瑜 , 等 . 基于跨平台的在线社交网络用户推荐研究 [J ] . 通信学报 , 2018 , 39 ( 3 ): 147 - 158 .

PENG J , WANG T T , CHEN Y , et al . User recommendation based on cross-platform online social networks [J ] . Journal on Communications , 2018 , 39 ( 3 ): 147 - 158 .

朱敬华 , 明骞 . LBSN 中融合信任与不信任关系的兴趣点推荐 [J ] . 通信学报 , 2018 , 39 ( 7 ): 157 - 165 .

ZHU J H , MING Q . POI recommendation by incorporating trust-distrust relationship in LBSN [J ] . Journal on Communications , 2018 , 39 ( 7 ): 157 - 165 .

孙光福 , 吴乐 , 刘淇 , 等 . 基于时序行为的协同过滤推荐算法 [J ] . 软件学报 , 2013 , 24 ( 11 ): 2721 - 2733 .

SUN G F , WU L , LIU Q , et al . Recommendations based on collabora-tive filtering by exploiting sequential behaviors [J ] . Journal of Software , 2013 , 24 ( 11 ): 2721 - 2733 .

浏览量

1155

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于粗糙集规则提取的协同过滤推荐算法

LBSN中融合信任与不信任关系的兴趣点推荐

基于用户模糊相似度的协同过滤算法

基于用户相似度的协同过滤推荐算法

基于主题的用户兴趣域算法