Semantic Modeling for Personalized Dietary Recommendation

Arens-Volland, Andreas; Gâteau, Benjamin; Naudet, Yannick

doi:10.1109/smap.2018.8501864

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…This trend well indicates the significance of recommendation systems for nutrition (Bianchini et al, 2017;Jung and Chung, 2016;Ribeiro et al, 2017), and the use of modern technology, such as the internet of things in this regard (Ali et al, 2018a;Jabeen et al, 2019). This is a clear indication that recommendation systems for nutrition and dietary health are gaining more attention from researchers as a way of improving user's overall state of health and well-being (Arens-Volland et al, 2018). Application of recommendation systems to this essential component of health and well-being is exemplified in such work as the one that introduced personalized expert recommendation system for optimized nutrition and helping users adapt their behaviors to healthier nutrition (Chen et al, 2018) as well as in techniques for making intelligent recommendation of dishes (Li et al, 2018).…”

Section: Recommendation Systems In Health Carementioning

confidence: 90%

“…As a result, they have become a focus of research in recent years. Efforts in this direction are seen in issues relating to general health-care interventions, nutrition and well-being (Ali et al, 2018a(Ali et al, , 2018bArens-Volland et al, 2018). Recommendation systems have also been applied in physical and mental health (Tene and Tangedipalli, 2018;Yang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Improving the design of a recommendation system using evaluation criteria and metrics as a guide

Afolabi

Toivanen

2019

JSIT

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Purpose The roles recommendation systems play in health care have become crucial in achieving effective care and in meeting the needs of modern care giving. As a result, efforts have been geared toward using recommendation systems in the management of chronic diseases. Effectiveness of these systems is determined by evaluation following implementation and before deployment, using certain metrics and criteria. The purpose of this study is to ascertain whether consideration of criteria during the design of a recommendation system can increase acceptance and usefulness of the recommendation system. Design/methodology/approach Using survey-style requirements gathering method, the specific health and technology needs of people living with chronic diseases were gathered. The result was analyzed using quantitative method. Sets of harmonized criteria and metrics were used along with requirements gathered from stakeholders to establish relationship among the criteria and the requirements. A matching matrix was used to isolate requirements for prioritization. These requirements were used in the design of a mobile app. Findings Matching criteria against requirements highlights three possible matches, namely, exact, inferential and zero matches. In any of these matches, no requirement was discarded. This allows priority features of the system to be isolated and accorded high priority during the design. This study highlights the possibility of increasing the acceptance rate and usefulness of a recommendation system by using metrics and criteria as a guide during the design process of recommendation systems in health care. This approach was applied in the design of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People. The result has shown that with this method, it is possible to increase acceptance rate, robustness and usefulness of the product. Research limitations/implications Inability to know the evaluation criteria beforehand, inability to do functional analysis of requirements, lack of well-defined requirements and often poor cooperation from people living with chronic diseases during requirements gathering for fear of stigmatization, confidentiality and privacy breaches are possible limitations to this study. Practical implications The result has shown that with this method, it is possible to isolate more important features of the system and use them during the design process, thereby speeding up the design and increasing acceptance rate, robustness and usefulness of the system. It also helps to see in advance the likely features of the system that will enhance its usefulness and acceptance, thereby increasing the confidence of the developers in their ability to deliver a system that will meet users’ needs. As a result, developers know beforehand where to concentrate their efforts during system development to ascertain the possibility of increasing usefulness and acceptance rate of a recommendation system. In addition, it will also save time and cost. Originality/value This paper demonstrates originality by highlighting and testing the possibility of using evaluation criteria and metrics during the design of a recommender system with a view to increasing acceptance and enhancing usefulness. It also shows the possibility of using the metrics and criteria in system’s development process for an exercise other than evaluation.

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Section: Recommendation Systems In Health Carementioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Improving the design of a recommendation system using evaluation criteria and metrics as a guide

Afolabi

Toivanen

2019

JSIT

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“…Apart from this mainstream, similar cases for knowledge integration can be made for less obvious domains, too, examples being food [15,218] or fashion [277,189]. Food KBs have integrated sources like the FoodOn ontology [136], the nutrients catalog https://catalog.data.…”

Section: Beyond Wikipediamentioning

confidence: 99%

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Weikum¹,

Dong²,

Razniewski³

et al. 2020

Preprint

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Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics.This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

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