research-article

Hierarchical Physician Recommendation via Diversity-enhanced Matrix Factorization

Authors:

Youtao ZhangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 1

Article No.: 1, Pages 1 - 17

https://doi.org/10.1145/3418227

Published: 07 December 2020 Publication History

Abstract

Recent studies have shown that there exhibits significantly imbalanced medical resource allocation across public hospitals. Patients, regardless of their diseases, tend to choose hospitals and physicians with a better reputation, which often overloads major hospitals while leaving others underutilized. Guiding patients to hospitals that can serve their treatment needs both timely and with good quality can make the best use of precious medical resources. Unfortunately, it remains one of the major challenges both for research and in practice. In this article, we propose a novel diversity-enhanced hierarchical physician recommendation approach to address this issue. We adopt matrix factorization to estimate physician competency and exploit implicit similarity relationships to improve the competency estimation of physicians that we are of little information of. We then balance the patient preference and physician diversity using two novel heuristic algorithms. We evaluate our proposed approach and compare it with the state of the art. Experiments show that our approach significantly improves both accuracy and recommendation diversity over existing approaches.

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Cited By

Jiang HMi ZXu W(2024)Online Medical Consultation Service–Oriented Recommendations: Systematic ReviewJournal of Medical Internet Research10.2196/4607326(e46073)Online publication date: 30-Jul-2024
https://doi.org/10.2196/46073
Wang SBian HLi S(2024)Hybrid Travel Recommendation Algorithm based on Center Aggregation Parameters2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)10.1109/ICICACS60521.2024.10498244(1-7)Online publication date: 23-Feb-2024
https://doi.org/10.1109/ICICACS60521.2024.10498244
Huang SZhang YFu LWang S(2023)Learnable Multi-View Matrix Factorization With Graph Embedding and Flexible LossIEEE Transactions on Multimedia10.1109/TMM.2022.315799725(3259-3272)Online publication date: 2023
https://doi.org/10.1109/TMM.2022.3157997
Show More Cited By

Index Terms

Hierarchical Physician Recommendation via Diversity-enhanced Matrix Factorization
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Combination, fusion and federated search
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 1

February 2021

361 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3441647

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 07 December 2020

Accepted: 01 July 2020

Revised: 01 May 2020

Received: 01 September 2019

Published in TKDD Volume 15, Issue 1

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Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China

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Cited By

Jiang HMi ZXu W(2024)Online Medical Consultation Service–Oriented Recommendations: Systematic ReviewJournal of Medical Internet Research10.2196/4607326(e46073)Online publication date: 30-Jul-2024
https://doi.org/10.2196/46073
Wang SBian HLi S(2024)Hybrid Travel Recommendation Algorithm based on Center Aggregation Parameters2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)10.1109/ICICACS60521.2024.10498244(1-7)Online publication date: 23-Feb-2024
https://doi.org/10.1109/ICICACS60521.2024.10498244
Huang SZhang YFu LWang S(2023)Learnable Multi-View Matrix Factorization With Graph Embedding and Flexible LossIEEE Transactions on Multimedia10.1109/TMM.2022.315799725(3259-3272)Online publication date: 2023
https://doi.org/10.1109/TMM.2022.3157997
Wang MLiang DCao WFu Y(2022)Physician recommendation via online and offline social network group decision making with cross-network uncertain trust propagationAnnals of Operations Research10.1007/s10479-022-04827-9341:1(583-619)Online publication date: 23-Jul-2022
https://doi.org/10.1007/s10479-022-04827-9
Huang SZhang YFu LWang S(2021)Enhanced Multi-view Matrix Factorization with Shared RepresentationPattern Recognition and Computer Vision10.1007/978-3-030-88013-2_23(276-287)Online publication date: 22-Oct-2021
https://doi.org/10.1007/978-3-030-88013-2_23

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