Conference proceeding
Personalized Recommendation in Online Health Communities with Heterogeneous Network Mining
2016 IEEE International Conference on Healthcare Informatics (ICHI), pp 281-284
01 Jan 2016
Abstract
There is an increasing attention of Online Health Communities (OHCs) where health consumers exchange informational and emotional support from their peers. However, the information overload issue makes it difficult for e-patients to identify relevant threads for their needs. An effective thread recommendation approach is highly desirable for OHCs to improve user experience. In this work, we propose to represent OHCs data as a Heterogeneous Healthcare Information Network (HHIN). We extract node-based and path-based features, and train a binary classification model for personalized thread recommendation. We conduct an experiment using a dataset from a popular OHC, and the results show that our approach is promising in predicting users' preference in threads.
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Details
- Title
- Personalized Recommendation in Online Health Communities with Heterogeneous Network Mining
- Creators
- Ling Jiang - Drexel UniversityChristopher C. Yang - Drexel UniversityIEEE
- Publication Details
- 2016 IEEE International Conference on Healthcare Informatics (ICHI), pp 281-284
- Conference
- 2016 IEEE International Conference on Healthcare Informatics (ICHI)
- Publisher
- IEEE
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000391422100037
- Scopus ID
- 2-s2.0-85010377753
- Other Identifier
- 991019168512504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Health Care Sciences & Services
- Medical Informatics