Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics 2018
DOI: 10.1145/3227609.3227685
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A Disease-driven Nutrition Recommender System based on a Multi-agent Architecture

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Cited by 4 publications
(5 citation statements)
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“…Numerous nutrition recommender systems have been designed and implemented worldwide, but they solely focus on users' nutritional needs and preferences and ignore economic factors when recommending meals or diets. 12,[25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47] Due to the contribution of economic factors to food security, Rules for pregnancy status • IF the user is pregnant, THEN remove Z 1 -Z n meals from the recommendations.…”
Section: Principal Resultsmentioning
confidence: 99%
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“…Numerous nutrition recommender systems have been designed and implemented worldwide, but they solely focus on users' nutritional needs and preferences and ignore economic factors when recommending meals or diets. 12,[25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47] Due to the contribution of economic factors to food security, Rules for pregnancy status • IF the user is pregnant, THEN remove Z 1 -Z n meals from the recommendations.…”
Section: Principal Resultsmentioning
confidence: 99%
“…There is still no consensus on the use of smart systems for recommending personalized diets; moreover, as this study aimed to promote food security, meal recommendation was regarded as the output of the recommender system, which is also the case in many other studies. 12,[25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47] After eliciting system requirements, based on the agile approach, the Web-based TanSa system was designed. This system provides two principal services "recommending healthy and affordable meals for individuals and households" and "offering a tele-nutrition consulting system".…”
Section: Principal Resultsmentioning
confidence: 99%
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“…It is observed that while some of the services recommend diets based on health conditions, most of them focus on a specific disease. Only a handful of research works 10,11,13,14 focus on different health scenarios by considering disease related parameters like BMI, calorie intake, and so forth. The prime emphasis of these works is based on chronic diseases like diabetes and obesity and not outlining a global scenario.…”
Section: Existing Literaturementioning
confidence: 99%
“…In recent years, researchers have introduced a few multimedia nutri-FRS involving advanced technologies, such as Embedded System, Multiagent Architecture, Internet of Things (IoT) and Smart-health. Some of the significant works in this category include: Ontology-driven Personalized FRS for IoT-based Healthcare System {Adel Taweel et al [177] }, Multi-agent Architecture based disease-driven nutri-FRS { Todor Ivascu et al [108] }, Smart-nudging based FRS to guide eating behavior of the older adult who has recently been diagnosed with type II diabetes { Wen-Yu Chao et al [67], SMASH usability heuristics and emotion detection based FRS { Tsaihsuan Tsai et al [174] }, central administration based cloud system { Shreya B. Ahire et al [148] }, Oracle Data Miner based treatment model prediction based FRS{ Abdullah A Aljumah et al [47] } and adaptive diet monitoring based FRS { Giuseppe Agapito et al [44] }. While impressive for combining electronic and RS technology, many of the works mentioned here only support fundamental FR strategies, certain target user groups, and fixed sets of food-items.…”
Section: Embedded System and Internet Of Things Platformsmentioning
confidence: 99%