Food Recommendation Using Ontology and Heuristics

El-Dosuky, M. A.; Rashad, M. Z.; Hamza, Taher; El-Bassiouny, A. H.

doi:10.1007/978-3-642-35326-0_42

Cited by 32 publications

(17 citation statements)

References 15 publications

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“…To provide recommendations to the users, we implemented a content-based recommender engine using a Python API. Products from the food products database were compared between pairs using a cosine similarity metric, which is commonly used for providing product recommendations [23]. A similarity index was generated for each of the products in the database, keeping the top-four similar products to provide a ranked list with recommendations for the user.…”

Section: Methodsmentioning

confidence: 99%

Designing Augmented Reality Applications for Personal Health Decision-Making

Gutiérrez¹,

Htun²,

Charleer³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Augmented reality (AR) is a technology that can assist with our daily decision-making tasks by presenting information that extends the physical world. However, little work has been done to understand the effect of the layout of AR interfaces on decision-making. In this paper, we present PHARA, an AR-based personal assistant that supports decision-making for healthier food products. In a controlled user study (n=28), we explored the use of four different AR layouts on two different devices: Microsoft HoloLens and smartphone. Using subjective and objective means, we measured their effects on decision-making tasks that occur when people hold food products in their hands. We found that pie and grid layouts perform better on the smartphone, whereas a stacked layout works better on the reduced field-of-view of the Microsoft HoloLens, potentially at the cost of some affordances such as time spent and actions.

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Section: Methodsmentioning

confidence: 99%

Designing Augmented Reality Applications for Personal Health Decision-Making

Gutiérrez¹,

Htun²,

Charleer³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…All research discussed in this subsection aims for recommending food items or menus to individual users on the basis of exploring user tastes. Most of them use popular recommendation techniques Svensson et al 2000;El-Dosuky et al 2012), and/or combine with different techniques in order to improve the quality of recommendation (Elahi et al 2015;Kuo et al 2012) (see Table 1). …”

Section: Type 1: Considering User Preferencesmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

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Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

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“…Because ingredients and recipes are written in un-or semi-structured forms, they are not necessarily amenable to immediate analysis, and recipe normalisation is known to be beneficial for downstream tasks [24,28]. Thus, early work employed ontologies or knowledge graphs [6,8,9], supervised training [28] and scoring methods [32] to extract clean elements. Most of these approaches require a labelled set of 'canonical' recipe entities (ingredients, tools, skills) and to date have been evaluated on a single language, which presents a problem for scaling the approaches to a multi-language system.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Towards Multi-Language Recipe Personalisation and Recommendation

Twomey¹,

Fain²,

Ponikar³

et al. 2020

Fourteenth ACM Conference on Recommender Systems

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Multi-language recipe personalisation and recommendation is an under-explored field of information retrieval in academic and production systems. The existing gaps in our current understanding are numerous, even on fundamental questions such as whether consistent and high-quality recipe recommendation can be delivered across languages. Motivated by this need, we consider the multi-language recipe recommendation setting and present grounding results that will help to establish the potential and absolute value of future work in this area. Our work draws on several billion events from millions of recipes, with published recipes and users incorporating several languages, including Arabic, English, Indonesian, Russian, and Spanish. We represent recipes using a combination of normalised ingredients, standardised skills and image embeddings obtained without human intervention. In modelling, we take a classical approach based on optimising an embedded bi-linear user-item metric space towards the interactions that most strongly elicit cooking intent. For users without interaction histories, a bespoke content-based cold-start model that predicts context and recipe affinity is introduced. We show that our approach to personalisation is stable and scales well to new languages. A robust cross-validation campaign is employed and consistently rejects baseline models and representations, strongly favouring those we propose. Our results are presented in a language-oriented (as opposed to model-oriented) fashion to emphasise the language-based goals of this work. We believe that this is the first large-scale work that evaluates the value and potential of multi-language recipe recommendation and personalisation.

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Food Recommendation Using Ontology and Heuristics

Cited by 32 publications

References 15 publications

Designing Augmented Reality Applications for Personal Health Decision-Making

Designing Augmented Reality Applications for Personal Health Decision-Making

An overview of recommender systems in the healthy food domain

Towards Multi-Language Recipe Personalisation and Recommendation

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