Deriving a recipe similarity measure for recommending healthful meals

Pinxteren, Youri van; Geleijnse, Gijs; Kamsteeg, Paul A.

doi:10.1145/1943403.1943422

Cited by 72 publications

(39 citation statements)

References 12 publications

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“…Although some systems were proposed to tackle with these problems, for instance, FOODLOG (Aizawa et al 2010), they are not able to give the accurate information about the consumed meals, even though they can estimate the nutritional balance among different kinds of food in a meal. Collecting user information -Taking advantage of information about users' previous meals (Van Pinxteren et al 2011). …”

Section: Challenges Regarding User Informationmentioning

confidence: 99%

“…-Collecting user rating data: Food recommender systems need information about users' preferences to recommend similar food items ((Van Pinxteren et al 2011;Mika 2011)). This information can be gathered by asking users to rate foods/recipes.…”

Section: Challenges Regarding User Informationmentioning

confidence: 99%

“…This problem can be surmounted by using information about users' previous meals to calculate similarity and then recommend new recipes to users (Van Pinxteren et al 2011). However, this solution requires many user efforts and abates the desire of system usage.…”

Section: Challenges Regarding Recommendation Algorithmsmentioning

confidence: 99%

“…Although most people are aware of the importance of healthy eating habits, they usually tend to neglect appropriate behaviors because of busy lifestyles and/or unwillingness to spend cognitive effort on food preparation. Those problems prevent users from a healthy food consumption (Van Pinxteren et al 2011). Hence, recommender systems are investigated as an effective solution in order to help users to change their eating behavior and to aim for healthier food choices.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Challenges Regarding User Informationmentioning

confidence: 99%

Section: Challenges Regarding User Informationmentioning

confidence: 99%

Section: Challenges Regarding Recommendation Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An overview of recommender systems in the healthy food domain

et al. 2017

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“…Freyne et al investigated three recommender strategies, which break down meals into ingredients for generating recommendations [2]. Pixteren et al derived a measure that quantifies the similarity between recipes by extracting important features from the recipe text [9].…”

Section: Related Workmentioning

confidence: 99%

Personalized Techniques for Lifestyle Change

Freyne

Berkovsky

Baghaei

et al. 2011

Artificial Intelligence in Medicine

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Abstract. Online delivery of lifestyle programs offers the potential to cost effectively reach large cohorts of users with various information and dietary needs. Unfortunately, online systems can fail to engage users in the long term, affecting their ability to sustain lifestyle change. In this work we present the initial analysis of a large scale application study of personalized technologies for lifestyle change. We evaluate the stickiness of an eHealth portal which provides individuals with three personalized tools -meal planner, social network feeds, and social comparison -to make change a reality in their lives. More than 5000 Australians took part in a 12 week study and provided solid empirical evidence for how the inclusion of personalized tools can assist and motivate users.

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