Traditional information recommendation system using only the user’s score is calculated and recommended, although to a certain extent, can obtain the implied characteristics of users or resources, but the lack of enough semantic interpretation, affecting the effects of recommendation. This article studied and analyzed the recommendation based on attribute coupled matrix decomposition algorithm in the application of Internet of things, on the foundation of the matrix decomposition model successively introduced global offset and time offset, in order to improve the prediction accuracy and the quality is recommended. In this paper, the algorithm is proved by experiment and the prediction accuracy of the algorithm is improved.
WeiJ., HeJ., ChenK., Collaborative filtering and deep learning based recommendation system for cold start items, Expert Systems with Applications69 (2017), 29–39.
2.
BlondelV.D., GuillaumeJ.L., LambiotteR., Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment2008(10) (2008), P10008.
3.
WangY., ZengP., YuH., Energy tree dynamics of smart grid based on industrial internet of things, International Journal of Distributed Sensor Networks9(8) (2013), 6.
4.
YanB.N., LeeT.S., LeeT.P., Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): A co-word analysis, Scientometrics105(2) (2015), 1285–1300.
5.
CuiP., LiuH., AggarwalC., Uncovering and predicting human behaviors, IEEE Intelligent Systems31(2) (2016), 77–88.
6.
HardoonD.R., SzedmakS., Shawe-TaylorJ., Canonical correlation analysis: An overview with application to learning methods, Neural computation16(12) (2004), 2639–2664.
7.
LiuC.H., ZhangZ., ChenM., Personalized multimedia recommendations for cloud-integrated cyber-physical systems, IEEE Systems Journal11(1) (2017), 106–117.
8.
SarwarB., KarypisG., KonstanJ., Incremental singular value decomposition algorithms for highly scalable recommender systems, Fifth International Conference on Computer and Information Science Citeseer, 2002, pp. 27–28.
9.
AdomaviciusG., TuzhilinA., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge & Data Engineering (6) (2005), 734–749.
10.
MaH., YangH., LyuM.R., Sorec: Social recommendation using probabilistic matrix factorization, Proceedings of the 17th ACM Conference on Information and Knowledge Management ACM, 2008, pp. 931–940.
11.
KaratzoglouA., AmatriainX., BaltrunasL., Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering, Proceedings of the Fourth ACM Conference on Recommender Systems ACM, 2010, pp. 79–86.
12.
CremonesiP., KorenY., TurrinR., Performance of recommender algorithms on top-n recommendation tasks, Proceedings of the Fourth ACM Conference on Recommender Systems ACM, 2010, pp. 39–46.
13.
SarwarB., KarypisG., KonstanJ., Analysis of recommendation algorithms for e-commerce, Proceedings of the 2nd ACM Conference on Electronic Commerce ACM, 2000, pp. 158–167.
14.
DohrA., Modre-OpsrianR., DrobicsM., The internet of things for ambient assisted living, Information Technology: New Generations (ITNG), 2010 seventh International Conference on IEEE, 2010, pp. 804–809.
15.
MiorandiD., SicariS., DeF., Pellegrini, Internet of things: Vision, applications and research challenges, Ad hoc, Networks10(7) (2012), 1497–1516.
16.
TeixeiraT., HachemS., IssarnyV., Service oriented middleware for the internet of things: A perspective, European Conference on a Service-Based Internet, Springer, Berlin, Heidelberg, 2011, pp. 220–229.
17.
LiS., Da XuL. and ZhaoS., The internet of things: A survey, Information Systems Frontiers17(2) (2015), 243–259.
18.
DsouzaC., AhnG.J., TaguinodM., Policy-driven security management for fog computing: Preliminary framework and a case study, Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on IEEE, 2014, pp. 16–23.
19.
QiupingW., ShunbingZ., ChunquanD., Study on key technologies of Internet of Things perceiving mine, Procedia Engineering26 (2011), 2326–2333.
20.
GershenfeldN., KrikorianR., CohenD., The internet of things, Scientific American291(4) (2004), 76–81.
