计算机科学 ›› 2017, Vol. 44 ›› Issue (2): 88-92.doi: 10.11896/j.issn.1002-137X.2017.02.011

• 2016 第十三届全国Web 信息系统及其应用学术会议 • 上一篇    下一篇

融合社交网络的单类个性化协同排序算法

李改,陈强,李磊,潘进财   

  1. 顺德职业技术学院电子与信息工程学院 顺德528333;中山大学数据科学与计算机学院 广州510006,中山大学数据科学与计算机学院 广州510006;广东第二师范学院计算机科学系 广州510303,中山大学数据科学与计算机学院 广州510006,顺德职业技术学院电子与信息工程学院 顺德528333
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然科学基金项目(61370186),广东省自然科学基金项目(2016A030310018),广东省科技计划项目(2014A010103040,4B010116001),广州市科技计划项目(201604010049,201510010203),广东第二师范学院教授博士科研专项(2015ARF25),佛山市机电专业群工程技术开发中心2015年第二批开放课题(2015-KJZX139),广东省大学生科技创新培育专项(G2016Z08)资助

One-class Personalized Collaborative Ranking Algorithm Incorporating Social Network

LI Gai, CHEN Qiang, LI Lei and PAN Jin-cai   

  • Online:2018-11-13 Published:2018-11-13

摘要: 单类个性化协同排序算法的研究的核心思想是把单类协同过滤问题当成排序问题来看待。之前的研究仅仅使用了隐式反馈数据来对推荐对象进行排序,这限制了推荐的准确度。随着在线社交网络的出现,为了进一步提高单类个性化协同排序算法的准确度,提出了一种新的融合社交网络的单类个性化协同排序算法。在真实的包含社交网络的2个数据集上的实验验证了该算法在各个评价指标下的性能均优于几个经典的单类协同过滤算法。实验证明,社交网络信息对于提高单类个性化协同排序算法的性能具有重要作用。

关键词: 推荐系统,协同排序,社交网络,单类协同过滤,隐式反馈数据

Abstract: The research’s key idea of one-class personalized collaborative Ranking Algorithm is to make use of partial order of items.In the early research of these problems,the training data are only implicit feedback dataset,this limits the sorting accuracy.With the advent of online social networks,in order to improve the performance of one-class personalized collaborative ranking algorithm,we proposed a new one-class personalized collaborative ranking algorithm incorporating social network.We conducted our experiment on two large real-world datasets with social information.The experiment results illustrate that our approach achieves a better performance than several traditional OCCF methods.Experiments also show that the social network information plays an important role in improving the performance of one-class perso-nalized collaborative ranking algorithm.

Key words: Recommended systems,Collaborative ranking,Social network,One-class collaborative filtering,Implicit feedback dataset

[1] LI G,OU W H.Pairwise Probabilistic Matrix Factorization forImplicit Feedback Collaborative Filtering [J].Neurocomputing,2016,4:17-25.
[2] LI G,WANG L Y,OU W H.Robust Personalized Ranking fromImplicit Feedback [J].International Journal of Pattern Recognition and Artificial Intelligence,2016,0(1):1-28.
[3] LI G,CHEN Q.Exploiting Explicit and Implicit Feedbacks for Personalized Ranking [J].Mathematical Problems in Enginee-ring,2016,2016:1-11.
[4] PAN R,ZHOU Y,CAO B,et al.One-class collaborative Filtering [C]∥Proceedings of the IEEE International Conference on Data Mining.2008:502-511.
[5] WANG C,BLEI D M.Collaborative topic modeling for Recommending scientic articles [C]∥Proceedings of the 2011 Con-ference of the Knowledge Discovery and Data Mining.California,2011:448-45.
[6] GU Q,ZHOU J,DING C.Collaborative filtering:WeightedNonnegative Matrix Factorization Incorporating User and Item Graphs [C]∥Proceedings of the 2010 SIAM Conference on Data Mining.2010:199-210.
[7] ZHEN Y,LI W,YEUNG D.TagiCofi:tag informed collabora- tive filtering [C]∥Proceedings of the Fifth ACM Conference on Recommender Systems.2009:69-76.
[8] MA H,ZHOU D Y,LIU C,et al.Recommendation Systemswith Social Regularization [C]∥Proceedings of the 4th ACM International Conference on Web Search and Data Mining.Hongkong,China:ACM,2011:287-296.
[9] ZHU J K,MA H,CHEN C,et al.Social Recommendation Using Low-Rank Semidefinite Program [C]∥Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence.San Francisco,USA:AAAI,2011:158-163.
[10] JAMALI M,ESTER M.A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks [C]∥Proceedings of the Twenty-third International Conference on Artificial Intelligence.Barcelona,Catalonia,Spain:ACM,2011:2644-2649.
[11] LU W,LOANNIDIS S,BHAGAT S,et al.Optimal Recommendations under Attraction,Aversion,and Social Influence [C]∥Proceedings of the 20nd International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM,2014:657-666.
[12] YAO W L,HE J,HUANG G Y,et al.SoRank:Incorporating Social Information into Learning to Rank Models for Recommendation [C]∥Proceedings of the 23th ACM International Conference on World Wide Web.Seoul,Korea:ACM 2014:409-410.
[13] DING X T,JIN X M,LI Y J,et al.Celebrity Recommendation with Collaborative Social Topic Regression [C]∥Proceedings of the Twenty-third International Conference on Artificial Intelligence.Beijing,China:ACM,2013:2612-2618.
[14] PURUSHOTHAM S,LIU Y,KUO C.Collaborative topic re-gression with social matrix factorization for recommendation systems [C]∥Proceedings of the 29th ACM Intenational Conference on Machine Learing.Edinburgh,Scotland,UK:ACM,2012:1255-1265.
[15] RENDLE S,FREUDENTHALER C,G ANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback [C]∥Proceedings of the 22nd International Conference on Uncertainty in Artificial Intelligence.Montreal,Canada,2009:452-461.
[16] PATEREK A.Improving regularized singular value decomposition for collaborative filtering [C]∥Proceedings of KDD Cup and Workshop.ACM Press,2007:39-42.
[17] HU Y,KOREN Y,VOLINSKY C.Collaborative filtering forimplicit feedback datasets [C]∥Proceedings of the IEEE International Conference on Data Mining.Pisa,Italy:IEEE,2008:263-272.
[18] PAN W K,CHEN L.GBPR:Group Preference based Bayesian Personalized Ranking for One-Class Collaborative Filtering [C]∥Proceedings of the Twenty-third International Conference on Artificial Intelligence.Beijing,China:ACM,2013:3007-3011.
[19] YU L,PAN R,LI Z F.Adaptive social similarities for recommender systems[C]∥Proceedings of the fifth ACM Conference on Recommender Systems (RecSys).2011:257-260.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!