Multidimensional Group Recommendations in the Health Domain
| Autor: | Kostas Stefanidis, Haridimos Kondylakis, Maria Stratigi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
semantic similarity
lcsh:T55.4-60.8 020205 medical informatics Computer science media_common.quotation_subject Health literacy 02 engineering and technology lcsh:QA75.5-76.95 Theoretical Computer Science Domain (software engineering) Semantic similarity 0202 electrical engineering electronic engineering information engineering Collaborative filtering lcsh:Industrial engineering. Management engineering Function (engineering) media_common Numerical Analysis group recommendations Behavior change Data science Preference Computational Mathematics Computational Theory and Mathematics recommendations 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science group aggregation Welfare |
| Zdroj: | Algorithms, Vol 13, Iss 3, p 54 (2020) Algorithms Volume 13 Issue 3 |
| ISSN: | 1999-4893 |
| DOI: | 10.3390/a13030054 |
| Popis: | Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients&rsquo welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents. |
| Databáze: | OpenAIRE |
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