AppNet: A Large-Scale Multi-layer Heterogeneous Complex App Network for Intelligent Program Search

Xiao, Jianmao; Chen, Shizhan; Feng, Zhiyong; Yang, Jian

doi:10.1007/978-3-030-17642-6_33

Cited by 1 publication

(1 citation statement)

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“…With the explosively increasing global old-age population, elderly care services have gradually become a key industry of social concern. Society's progress, especially the revolution and development of the Internet (Dana et al, 2022;Xiao et al, 2020a) and intelligent software (Xiao et al, 2018), has spawned a range of pension services, which has crucial impacts on older people's service selection. However, due to their particularity, the elderly group has some distinctive characteristics such as health problems, old knowledge structure, and unskilled operation of electronic products when surfing the Internet.…”

Section: Introductionmentioning

confidence: 99%

Recommendation of Healthcare Services Based on an Embedded User Profile Model

Xiao

Liu²,

Zeng

et al. 2022

International Journal on Semantic Web and Information Systems

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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.

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Section: Introductionmentioning

confidence: 99%