提出了一种基于极速学习机和最近邻的协同过滤回归推荐算法.该算法首先采用k最近邻法对评分矩阵的缺失值进行填充,然后将极速学习机作为回归器为用户产生推荐.在推荐领域中的标杆数据集上,将该算法与常用推荐算法- LRCF算法进行了比较,验证了该算法的有效性.
This paper proposed a collaborative filtering regression recommendation algorithm based on extreme learning machine(ELM)and nearest neighbors.The algorithm firstly used the k nearest neighbors method to fill the missing attribute value,then the ELM regression machine is used to produce recommendations for the user.On the benchmarking data sets in the field of recommendation,we compared our algorithm with the common recommend algorithm—LRCF algorithm,and verified the effectiveness of the proposed algorithm.
[1] RESNICK P,VARIAN H R.Recommender systems[J].Communications of the ACM,1997,40(3):56-58.
[2] BREESE J S,HECKERMAN D,KADIE C.Empirical analysis of predictive algorithms for collaborative filtering[Z].The 14th Conference on Uncertainty in Artificial Intelligence,Madison,wisconsin,USA,1998.
[3] BASU C,HIRSH H,COHEN W,et al.Recommendation as classification: Using social and content-based information in recommendation[Z].The 15th National Conference on Artificial Intelligence and and 10th Innovative Applications of Artificial Intelligence Conference,Madison,Wisconsin,USA,1998.
[4] 马宏伟,张光卫,李鹏.协同过滤推荐算法综述[J].小型微型计算机系统,2009,30( 7): 1282-1288.
[5] 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.DOI:10.3724/SP.J.1001.2013.04478. SUN G F,WU L,LI Q,et al.Recommendations based on collaborative filtering by exploiting sequential behaviors[J].Journal of Software,2013,24(11):2721-2733.DOI:10.3724/SP.J.1001.2013.04478.
[6] 荣辉桂,火生旭,胡春华,等.基于用户相似度的协同过滤推荐算法[J].通信学报,2014,35(2):16-24.DOI:10.3969/j.issn.1000-436x.2014.02.003.
[7] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning machine: a new learning scheme of feedforward neural networks[C] //2004 IEEE International Joint Conference on Neural Networks.2004.DOI:10.1109/IJCNN.2004.1380068.
[8] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning machine:Theory and applications[J].Neurocomputing,2006,70(1-3):489-501.DOI:10.1016/j.neucom.2005.12.126.
[9] 李纯果,李海峰.无监督排序学习算法的一致性比较 [J].河北大学学报(自然科学版),2015,35(2): 182-187.DOI: 10.3969/j.issn.10001565.2015.02.013. LI C G,LI H F.Comparison analysis on ranking consensus[J].Journal of Hebei University: Naturnal Science Edition,2015,35(2): 182-187.DOI: 10.3969/j.issn.10001565.2015.02.013.
[10] 尚田丰.协同推荐关键技术研究[D].北京:中国科学院大学,2014. SHANG T F. Research on critical technologies of collaborative recommendation[D].Bei jing:University of chinese Academy of sciences,2014.
