Journal of University of Chinese Academy of Sciences >
An improved recommendation algorithm for mobile health care system
Received date: 2016-04-28
Revised date: 2016-05-18
Online published: 2017-01-15
As an important method for information searching and filtering, the mobile health care system has successfully improved service efficiency of existing medical resources. To enhance accuracy and effect of the information recommendation, a new recommendation algorithm based on multi-bipartite network is proposed. Evaluation criteria of users are enriched as "interested", "uninterested", and "unknown". Moreover, a genre layer is appended so that the information confidence propagates on a new multi-bipartite network iteratively. Finally, we prove that multi-bipartite network iterative algorithm enhances service effect of mobile health care system.
ZHOU Yan , LEI Shiyao , ZHANG Can . An improved recommendation algorithm for mobile health care system[J]. Journal of University of Chinese Academy of Sciences, 2017 , 34(1) : 112 -118 . DOI: 10.7523/j.issn.2095-6134.2017.01.015
[1] Deshpande M, Karypis G. Item-based top-N, recommendation algorithms[J]. ACM Transactions on Information Systems, 2014, 22(1):143-177.
[2] Javari A, Gharibshah J, Jalili M. Recommender systems based on collaborative filtering and resource allocation[J]. Social Network Analysis & Mining, 2014, 4(1):1-11.
[3] Balabanovi?M, Shoham Y. Fab:content-based, collaborative recommendation[J]. Communications of the ACM, 1997, 40(3):66-72.
[4] Pazzani M J, Billsus D. Content-based recommendation systems[C]//Springer-Verlag, LNCS, 2007,4321:325-341.
[5] Zhou T, Ren J, MEDO Matuo, et al. Bipartite network projection and personal recommendation[J]. Physical Review E, 2007, 76(4):70-80.
[6] Yin F, Zhao X, Zhang X, et al. Improving accuracy and scalability of personal recommendation based on bipartite network projection[J]. Mathematical Problems in Engineering, 2014:823749.
[7] Zhang X M, Jiang S Y. Personalized recommendation algorithm based on weighted bipartite network[J]. Journal of Computer Applications, 2012, 32(3):654-653.
[8] Guo Q, Song W J, Hou L, et al. Effect of the time window on the heat-conduction information filtering model[J]. Physica A Statistical Mechanics & Its Applications, 2014, 401(5):15-21.
[9] Fouss F, Pirotte A, Renders J M, et al. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation[J]. Knowledge & Data Engineering IEEE Transactions on, 2007, 19(3):355-369.
[10] Sadja A, Tang J, Mihalov B, et al. System and method for providing recommendations to a user in a viewing social network:US, US 20120117167 A1. 2012.
[11] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. IEEE Computer, 2009, 42(8):30-37.
[12] Jamali M, Ester M. A transitivity aware matrix factorization model for recommendation in social networks[C]//IJCAI 2011, Proceedings of the International Joint Conference on Artificial Intelligence. Barcelona, Catalonia, Spain. 2011:2644-2649.
[13] Schein A I, Popescul A, Ungar L H, et al. Methods and metrics for cold-start recommendations[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2002:253-260.
[14] Miller B N, Albert I, Lam S K, et al. MovieLens unplugged:experiences with an occasionally connected recommender system[C]//International Conference on Intelligent User Interfaces. ACM, 2003:263-266.
[15] Davis J, Goadrich M. The relationship between precision-recall and ROC curves[C]//Proceedings of the 23rd international conference on Machine learning. ACM, 2010:233-240.
/
| 〈 |
|
〉 |