Computer Science ›› 2018, Vol. 45 ›› Issue (4): 215-219.doi: 10.11896/j.issn.1002-137X.2018.04.036

Previous Articles     Next Articles

Study on Matrix Factorization Recommendation Algorithm Based on User Clustering and Mobile Context

WEN Jun-hao, SUN Guang-hui and LI Shun   

  • Online:2018-04-15 Published:2018-05-11

Abstract: With the rapid development of mobile Internet technology,more and more individuals use mobile devices to acquire information and services,which makes information overload problem more and more serious.Aiming at the puzzle resulted from data sparsity,insufficient contextual information and ignoring context similarity measurement,this paper proposesd a method of matrix factorization recommendation algorithm based on user clustering and mobile context(UCMC-MF) to predict user ratings and make recommendation.Firstly,the method clusters similar user by way of k-means,then finds similar contexts in each cluster,and searches users who are similar to the target user in preferences and context.Finally,experimental results on real datasets demonstrate that the proposed algorithm can effectively improve the accuracy of prediction.

Key words: Clustering,Context information,Matrix factorization,Recommendation

[1] MENG X W,HU X,WANG L C,et al.Mobile Recommender Systems and Their Appliations[J].Journal of Software,2013,24(1):91-108.(in Chinese) 孟祥武,胡勋,王立才,等.移动推荐系统及其应用[J].软件学报,2013,4(1):91-108.
[2] WANG Z M,YANG F.An optimized location-based mobile restaurant recommend and navigation system[J].Wseas Transactions on Information Science & Applications,2009,6(5):809-818.
[3] GIRARDELLO A,MICHAHELLES F.AppAware:which mobile applications are hot?[C]∥Conference on Human-Compu-ter Interaction with Mobile Devices and Services(Mobile Hci 2010).Lisbon,Portugal,DBLP,2010:431-434.
[4] TONG Q L,PARK Y,PARK Y T.A time-based approach to effective recommender systems using implicit feedback[J].Expert Systems with Applications,2008,34(4):3055-3062.
[5] SALAKHUTDINOV R,MNIH A.Probabilistic matrix factorization[C]∥International Conference on Machine Learning.2007:880-887.
[6] TU D D,SHU C C,YU H Y.Using Unified Probabilistic Matrix Factorization for Contextual Advertisement Recommendation[J].Journal of Software,2013,4(3):454-464.(in Chinese) 涂丹丹,舒承椿,余海燕.基于联合概率矩阵分解的上下文广告推荐算法[J].软件学报,2013,4(3):454-464.
[7] SCHILIT B,ADAMS N,WANT R.Context-Aware Computing Applications[C]∥First Workshop on Mobile Computing Systems and Applications.IEEE Computer Society,1994:85-90.
[8] ADOMAVICIUS G,TUZHILIN A.Context-aware recommender systems[C]∥ACM Conference on Recommender Systems.ACM,2008:335-336.
[9] HAI B Z,XIE R Y.Bayesian Network-based Context-awareRecommendation Algorithm[J].Computer Science,2014,41(7):275-278.(in Chinese) 海本斋,解瑞云.基于贝叶斯网络的上下文推荐算法[J].计算机科学,2014,1(7):275-278.
[10] ZHENG Y,MOBASHER B,BURKE R.Incorporating context correlation into context-aware matrix factorization[C]∥International Workshop on Intelligent Personalization.2015:21-27.
[11] MA H,ZHOU D,LIU C,et al.Recommender systems with social regularization[C]∥Forth International Conference on Web Search and Web Data Mining(WSDM 2011).Hong Kong,China,DBLP,2011:287-296.
[12] DAO T H,JEONG S R,AHN H.A novel recommendation mo-del of location-based advertising:Context-Aware Collaborative Filtering using GA approach[J].Expert Systems with Applications,2012,39(3):3731-3739.
[13] LIU R S,YANG T C.Improving Recommendation Accuracy by Considering Electronic Word-of-Mouth and the Effects of Its Propagation Using Collective Matrix Factorization[C]∥IEEE Datacom.IEEE,2016.
[14] ZHEN G L,ZHU F X,YAO X.Recommendation Rating Prediction Based on Attribute Boosting with Partial Sampling[J].Chinese Journal of Computers,2016,9(8):1501-1514.(in Chinese) 郑麟,朱福喜,姚杏.基于属性提升与局部采样的推荐评分预测[J].计算机学报,2016,9(8):1501-1514.
[15] EL-MOEMEN S A,HASSAN T,SEWISY A A.A Context-Aware Recommender System for Personalized Places in Mobile Applications[J].International Journal of Advanced Computer Science & Applications,2016,7(3):442-448.
[16] CHAMPIRI Z D,SHAHAMIRI S R,SALIM S S B.A systema-tic review of scholar context-aware recommender systems[J].Expert Systems with Applications,2015,42(3):1743-1758.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[3] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[4] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[5] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[6] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[7] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[8] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[9] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[10] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .