Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering
Joint Authors
Wang, Zongmin
Gao, Shan
Guo, Guibing
Li, Runzhi
Source
Journal of Healthcare Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gao, Shan& Guo, Guibing& Li, Runzhi& Wang, Zongmin. 2017. Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1181119
Modern Language Association (MLA)
Gao, Shan…[et al.]. Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering. Journal of Healthcare Engineering No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1181119
American Medical Association (AMA)
Gao, Shan& Guo, Guibing& Li, Runzhi& Wang, Zongmin. Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1181119
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181119