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KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums

Authors:

Dongwon LeeAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 492 - 501

https://doi.org/10.1145/3477495.3532008

Published: 07 July 2022 Publication History

Abstract

Health thread recommendation methods aim to suggest the most relevant existing threads for a user. Most of the existing methods tend to rely on modeling the post contents to retrieve relevant answers. However, some posts written by users with different clinical conditions can be lexically similar, as unrelated diseases (e.g., Angina and Osteoporosis) may have the same symptoms (e.g., back pain), yet irrelevant threads to a user. Therefore, it is critical to not only consider the connections between users and threads, but also the descriptions of users' symptoms and clinical conditions. In this paper, towards this problem of thread recommendation in online healthcare forums, we propose a knowledge graph enhanced Threads Recommendation (KETCH) model, which leverages graph neural networks to model the interactions among users and threads, and learn their representations. In our model, the users, threads and posts are three types of nodes in a graph, linked through their associations. KETCH uses the message passing strategy by aggregating information along with the network. In addition, we introduce a knowledge-enhanced attention mechanism to capture the latent conditions and symptoms. We also apply the method to the task of predicting the side effects of drugs, to show that KETCH has the potential to complement the medical knowledge graph. Comparing with the best results of seven competing methods, in terms of MRR, KETCH outperforms all methods by at least 0.125 on the MedHelp dataset, 0.048 on the Patient dataset and 0.092 on HealthBoards dataset, respectively. We release the source code of KETCH at: https://github.com/cuilimeng/KETCH.

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https://doi.org/10.62762/TSCC.2024.898503
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
Li HZhang ZWang XZhu W(2023)Invariant Node Representation Learning under Distribution Shifts with Multiple Latent EnvironmentsACM Transactions on Information Systems10.1145/360442742:1(1-30)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3604427
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Index Terms

KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

An YTan YSun XFerrari G(2024)Recommender System: A Comprehensive Overview of Technical Challenges and Social ImplicationsIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.8985031:1(30-51)Online publication date: 15-Oct-2024
https://doi.org/10.62762/TSCC.2024.898503
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
Li HZhang ZWang XZhu W(2023)Invariant Node Representation Learning under Distribution Shifts with Multiple Latent EnvironmentsACM Transactions on Information Systems10.1145/360442742:1(1-30)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3604427
Tan XWu WLuo CChen HDuh WHuang HKato MMothe JPoblete B(2023)SCHash: Speedy Simplicial Complex Neural Networks via Randomized HashingProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591762(1609-1618)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591762
Choi DXiang AOzturk OShrestha DDrake BHaidarian HJaved FPark H(2023)WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386138(616-625)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386138
Wei YChen WWen SLiu AZhao L(2023)Knowledge graph incremental embedding for unseen modalitiesKnowledge and Information Systems10.1007/s10115-023-01868-965:9(3611-3631)Online publication date: 20-Apr-2023
https://dl.acm.org/doi/10.1007/s10115-023-01868-9
Liu YZhong ZChe CZhu Y(2022)Recommendations with residual connections and negative sampling based on knowledge graphsKnowledge-Based Systems10.1016/j.knosys.2022.110049258:COnline publication date: 22-Dec-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.110049

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