收稿日期: 2016-04-28
修回日期: 2016-05-18
网络出版日期: 2017-01-15
基金资助
国家自然科学基金（61571416）资助
An improved recommendation algorithm for mobile health care system
Received date: 2016-04-28
Revised date: 2016-05-18
Online published: 2017-01-15
周岩 , 雷世尧 , 张灿 . 面向移动健康医疗系统的多层二分网络推荐算法[J]. 中国科学院大学学报, 2017 , 34(1) : 112 -118 . DOI: 10.7523/j.issn.2095-6134.2017.01.015
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.
[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.
/
| 〈 |
|
〉 |
