A Recommender System Based on Hesitant Fuzzy Linguistic Information with MAPPACC Approach

Xu, Zeshui; Chen, Hongyu; He, Yue

doi:10.24846/v29i2y202001

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Data sources of uneven quality can also affect the accuracy of sentiment evaluations. The fuzzy analysis of the text can help address the uncertainty of text description (Xu et al, 2020). The fuzzy analysis mainly applies fuzzy mathematical or fuzzy linguistic methods, which allow recommender systems to express uncertainty and obtain personalized features from patient comments.…”

Section: Feature Extractionmentioning

confidence: 99%

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Online Medical Consultation Service–Oriented Recommendations: Systematic Review (Preprint)

Jiang¹,

Xu²

2023

Preprint

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BACKGROUND A growing telehealth industry, the online health community (OHC) bridges the gap between physicians and patients by offering an e-service of a new sort through telemedicine - online medical consultation (OMC), a solution that alleviates problems associated with unbalanced distribution and inadequate high-quality medical resources. OBJECTIVE A personalized recommendation for OMC services is imperative to reduce patient information overload and optimize physician resource utilization. This paper aims at providing an overview of current trends and outlining e-Service-oriented paradigms and approaches to recommending OMCs. METHODS Our systematic literature search was limited to quantitative, qualitative, and mixed-method papers published from 2001 to 2020 through ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. Using cross-disciplinary literature on medicine, information systems, and artificial intelligence, this paper summarizes personalized recommendations for online services, which involve many aspects, such as two-sided matching for patients and physicians, interpretable recommendations for patients, and workload balancing for physicians. RESULTS The paper finds that e-service-oriented recommendations are an emerging concept that has not yet been clearly defined and fully investigated. We try to break the inertia associated with tinkering with the traditional personalized recommendation models, establish an innovative theoretical framework for e-service-oriented recommendations, and propose some critical technical issues of two-sided personalized recommendations. CONCLUSIONS OMC is a knowledge-intensive and labor-intensive service, where patients lack expert knowledge and demand interpretable recommendations; physicians have varied energy levels and cannot afford overwork. E-service recommendations need to face two-sided users with different cognitive abilities, expectation levels, decision-making perspectives, and preferences, so they require an entirely different paradigm needs to develop distinct attributes and study unique contents.

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Section: Feature Extractionmentioning

confidence: 99%

“…(2020) converted raw data into IFNs to describe uncertainty information by combining the patient's disease description with comments. Xu et al (2020) examined data based on hesitant fuzzy language multi-criteria preference analysis to enhance patient preferences for physician recommendations.…”

Section: Feature Extractionmentioning

confidence: 99%

Online Medical Consultation Service–Oriented Recommendations: Systematic Review (Preprint)

Jiang¹,

Xu²

2023

Preprint

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“…30 Many variants of HFLTSs have been put forward, such as the double hierarchy linguistic term set (LTS), 31,32 unbalanced HFLTS, 33 hesitant probabilistic fuzzy linguistic set, 34 and hesitant multiplicative LTSs. 35 HFLTSs have various applications, such as the evaluation of landfill sites, 36 medicine selection, 37 recommender system, 38 and healthcare service. 39 To handle the unique structure of HFLTSs, various decision-making methods have been proposed, such as the MULTIMOORA method, 40 aggregation operators, 28 TOPSIS method, 41 VIKOR method, 42,43 EDAS method, 44 and ELECTRE II method.…”

Section: Studies On Handling Hfltssmentioning

confidence: 99%

“…Many variants of HFLTSs have been put forward, such as the double hierarchy linguistic term set (LTS), 31,32 unbalanced HFLTS, 33 hesitant probabilistic fuzzy linguistic set, 34 and hesitant multiplicative LTSs 35 . HFLTSs have various applications, such as the evaluation of landfill sites, 36 medicine selection, 37 recommender system, 38 and healthcare service 39 …”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of startup companies using multicriteria decision making based on hesitant fuzzy linguistic information envelopment analysis models

Lin

Chen

et al. 2021

Int J Intell Syst

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Evaluating startup companies is an important management process for technology business incubators and it is also a typical multicriteria decision‐making (MCDM) problem. There exist various methods that have proposed to solve MCDM problems, but these methods heavily depend on the exact criteria weight values. The decision results of these methods are unstable. Moreover, they cannot provide the improvement suggestions for the nonoptimal startup companies. To overcome these two drawbacks, we propose a novel hesitant fuzzy linguistic decision‐making method to solve the problem of evaluating startup companies. To this end, a novel semantic comparison method based on the experts' psychology and the ratio of score value to deviation degree is proposed to compare the hesitant fuzzy linguistic term sets. Then, a novel definition of hesitant fuzzy linguistic information envelopment efficiency (HFLIEE) is proposed, based on which, a novel hesitant fuzzy linguistic information envelopment analysis (HFLIEA) model and a novel preference model are proposed. By solving these models, all the alternatives can be ranked and nonoptimal alternatives can be improved. Finally, the numerical analysis is given to illustrate the applicability of the proposed models and the robustness analyses of the proposed models are provided. At the same time, they are compared with the previous hesitant fuzzy linguistic decision‐making methods.

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A survey of collaborative decision-making: Bibliometrics, preliminaries, methodologies, applications and future directions

Cai

Jin

Liu

et al. 2023

Engineering Applications of Artificial Intelligence

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A Recommender System Based on Hesitant Fuzzy Linguistic Information with MAPPACC Approach

Cited by 5 publications

References 14 publications

Online Medical Consultation Service–Oriented Recommendations: Systematic Review (Preprint)

Online Medical Consultation Service–Oriented Recommendations: Systematic Review (Preprint)

Evaluation of startup companies using multicriteria decision making based on hesitant fuzzy linguistic information envelopment analysis models

A survey of collaborative decision-making: Bibliometrics, preliminaries, methodologies, applications and future directions

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