计算机科学 ›› 2015, Vol. 42 ›› Issue (12): 272-274.

• 人工智能 • 上一篇    下一篇

基于线性学习模型的社会媒体流排名算法

张威,李跃新   

  1. 湖北大学计算机与信息工程学院 武汉430064,湖北大学计算机与信息工程学院 武汉430064
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(61170306),湖北省科技支撑项目(2014BAA089)资助

Learning to Rank Based on Linear Model for Social Media Streams

ZHANG Wei and LI Yue-xin   

  • Online:2018-11-14 Published:2018-11-14

摘要: 在社会媒体中,对用户推荐适合的状态更新不仅降低了用户搜索信息的时间,也可以增加用户对服务的粘性。针对社会媒体中状态更新而推荐的准确性低的不足,提出了一种基于线性学习模型的状态更新排名算法。首先,根据社会媒体的性质定义了相应的偏好属性,并提出了一种基于线性模型的潜在偏好模型;其次,根据状态更新以及接收者的特征定义了相应的线性特征模型;最后,将潜在偏好模型和特征模型相结合,提出了一种引入时间效应的线性模型。通过实验验证表明,提出的算法与其它相关算法相比,算法的预测准确性更高,执行效率更快。

关键词: 社会媒体流,排名算法,排名学习,线性模型

Abstract: In social media,recommending suitable updates for users can not only reduce information searching time,but also improve users’ stickiness for social media.In order to improve the recommendation accuracy of updates in social media,this paper proposed a linear model based learning to ranking algorithm for updates.Firstly,according to attribu-tes of social media,we defined corresponding bias features,and proposed a linear model based latent bias model.Se-condly,according to features of update and recipients,we defined corresponding linear feature model.Finally,combining the latent bias model and the feature model,we proposed a linear model with temporal effect.The experiments show that,compared with related works,the proposed algorithm has better prediction accuracy and higher execution efficiency.

Key words: Social media streaming,Ranking,Learn to rank,Linear model

[1] Kaplan A M,Haenlein M.Users of the world,unite! The challenges and opportunities of Social Media[J].Business horizons,2010,53(1):59-68
[2] 徐恪,张赛,陈昊,等.在线社会网络的测量与分析[J].计算机学报,2014,37(1):165-188 Xu Ke,Zhang Sai,Chen Hao,et al.Measurement and Analysis of Online Social Networks[J].Chinese Journal of Computers,2014,37(1): 165-188
[3] 陈晓江,房鼎益,刘炜,等.基于 CORBA 的媒体流构件模型[J].西北大学学报(自然科学版),2005,35(2):151-154 Chen Xiao-jiang,Fang Ding-yi,Liu Wei,et al.Media streamcomponent model based on CORBA[J].Journal of Northwest University(Natural Science),2005,35(2):151-154
[4] Paris C,Wan S.Listening to the community:social media monitoring tasks for improving government services[C]∥Procee-dings of the International Conference on Human Factors in Computing Systems(CHI 2011).Extended Abstracts Volume,Vancouver,BC,2011:2095-2100
[5] Braunstein S L,Pirandola S,Zyczkowski K.Better late than ne-ver:information retrieval from black holes[J].Physical review letters,2013,110(10):101-108
[6] Fercoq O,Akian M,Bouhtou M,et al.Ergodic control and polyhedral approaches to PageRank optimization[J].IEEE Transactions on Automatic Control,2013,58(1):134-148
[7] Venkatraman V,Ritchie D W.Flexible protein docking refine-ment using pose-dependent normal mode analysis[J].Proteins:Structure,Function,and Bioinformatics,2012,80(9):2262-2274
[8] Sakakura Y,Yamaguchi Y,Amagasa T,et al.A Local Method for ObjectRank Estimation[C]∥Proceedings of International Conference on Information Integration and Web-based Applications & Services.ACM,2013:92-98
[9] Cao L,Cho B,Kim H D,et al.Delta-SimRank Computing on MapReduce[C]∥BigMine’12,2012.New York,NY,USA,2012:28-35
[10] 李栋,徐志明,李生,等.在线社会网络中信息扩散[J].计算机学报,2014,37(1):189-206 Li Dong,Xu Zhi-ming,Li Sheng,et al.A survey on Information Diffusion in Online Social Networks[J].Chinese Journal of Computers,2014,37(1):189-206
[11] Zanardi V,Capra L.Uncovering Relevant Content Using Tag-based Recommender Systems[C]∥RecSys’08,2008.New York,NY,USA,2008:51-58
[12] Sharma S K,Suman U.A Trust-based Architectural Framework for Collaborative Filtering Recommender System[J].Int.J.Bus.Inf.Syst.,2014,16(2):134-153
[13] Carrer-Neto W,María L,Valencia-García R,et al.Social Know-ledge-based Recommender System[J].Application to the Movies Domain.Expert Syst.Appl.,2012,39(12):10990-11000
[14] Di Noia T,Mirizzi R,Ostuni V,et al.Exploiting the Web of Data in Model-based Recommender Systems[C]∥RecSys’12,2012.New York,NY,USA,2012:253-256
[15] Wang B,Liao Q,Zhang C.Weight Based KNN Recommender System[C]∥IHMSC’13,2013.Washington DC,USA,2013:449-452
[16] Verberne S,Halteren H,Theijssen D,et al.Learning to Rank for Why-question Answering[J].Inf.Retr.,2011,14(2):107-132
[17] 卞先华,陈亮,郑倩冰.基于文本内容和社会结构的可信度[J].重庆理工大学学报(自然科学版),2013,27(1):57-61 Bian Xian-hua,Chen Liang,Zheng Qian-bing.Reliability Research Based on Text Context and Community Structure[J].Journal of Chongqing University of Technology (Natural Science),2013,27(1):57-61

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