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Recommendation of Healthcare Services Based on an Embedded User Profile Model

Jianmao Xiao, Xinyi Liu, Jia Zeng, Yuanlong Cao, Zhiyong Feng

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.313198

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MLA

Xiao, Jianmao, et al. "Recommendation of Healthcare Services Based on an Embedded User Profile Model." IJSWIS vol.18, no.1 2022: pp.1-21. http://doi.org/10.4018/IJSWIS.313198

APA

Xiao, J., Liu, X., Zeng, J., Cao, Y., & Feng, Z. (2022). Recommendation of Healthcare Services Based on an Embedded User Profile Model. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-21. http://doi.org/10.4018/IJSWIS.313198

Chicago

Xiao, Jianmao, et al. "Recommendation of Healthcare Services Based on an Embedded User Profile Model," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-21. http://doi.org/10.4018/IJSWIS.313198

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Abstract

In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a service recommendation framework PCE-CF based on an embedded user portrait model. The framework accurately describes the elderly users through four dimensions—population, society, consumption, and health—and constructs the user portrait model by embedding tags. The embedded vector of each older man is learned through the deep learning model, and different feature groups are meaningfully expressed in the transformation space. In addition, location context and dynamic interest model are introduced to process embedded vectors, and users' service preferences are predicted according to their dynamic behaviors. The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.