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Journal of Healthcare Engineering
Volume 2017, Article ID 5967302, 11 pages
https://doi.org/10.1155/2017/5967302
Research Article

Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering

1School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
3Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
4Software College, Northeastern University, Shenyang 110169, China

Correspondence should be addressed to Guibing Guo; nc.ude.uen.cws@bgoug and Zongmin Wang; nc.ude.uzz@gnawmz

Received 14 November 2016; Accepted 6 August 2017; Published 3 October 2017

Academic Editor: Maria Lindén

Copyright © 2017 Shan Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nowadays, providing high-quality recommendation services to users is an essential component in web applications, including shopping, making friends, and healthcare. This can be regarded either as a problem of estimating users’ preference by exploiting explicit feedbacks (numerical ratings), or as a problem of collaborative ranking with implicit feedback (e.g., purchases, views, and clicks). Previous works for solving this issue include pointwise regression methods and pairwise ranking methods. The emerging healthcare websites and online medical databases impose a new challenge for medical service recommendation. In this paper, we develop a model, MBPR (Medical Bayesian Personalized Ranking over multiple users’ actions), based on the simple observation that users tend to assign higher ranks to some kind of healthcare services that are meanwhile preferred in users’ other actions. Experimental results on the real-world datasets demonstrate that MBPR achieves more accurate recommendations than several state-of-the-art methods and shows its generality and scalability via experiments on the datasets from one mobile shopping app.