Yum-Me

Yang, Longqi; Hsieh, Cheng-Kang; Yang, Hongjian; Pollak, John P.; Dell, Nicola; Belongie, Serge; Cole, Curtis L.; Estrin, Deborah

doi:10.1145/3072614

Cited by 119 publications

(16 citation statements)

References 57 publications

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“…Although all recommended items were health related, only Asthana et al [46] explicitly mentioned using electronic health record data. Only 14% (10/73) [7,[47][48][49][50][51][52][53][54][55] explicitly reported that they addressed the cold-start problem.…”

Section: Study Detailsmentioning

confidence: 99%

Health Recommender Systems: Systematic Review

Croon¹,

Houdt²,

Htun³

et al. 2021

J Med Internet Res

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Background Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. Methods We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Results Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. Conclusions There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

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Section: Study Detailsmentioning

confidence: 99%

Health Recommender Systems: Systematic Review

Croon¹,

Houdt²,

Htun³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…For example, recipes usually not only comprise a title and a list of ingredients (Van Erp et al, 2020), but also directions, images (Elsweiler et al, 2017), and sometimes even videos (Min et al, 2016;Chen et al, 2017). As a result, a considerable proportion of research is devoted to optimizing the retrieval of recipes, particularly in terms of relevance and efficiency (Helmy et al, 2015;Min et al, 2016;Trattner and Elsweiler, 2017a;Yang et al, 2017).…”

Section: Food Searchmentioning

confidence: 99%

Nudging Healthy Choices in Food Search Through Visual Attractiveness

Starke

Willemsen

Trattner

2021

Front. Artif. Intell.

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Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction.

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“…In the meal planning example, confidence can be measured using the ratio of shared ingredients between recipes. Other solutions can include common flavour combinations, co-occurrence of ingredients as in IBM Chef Watson [23], or health benefits as in Yum-me [29].…”

Section: Resource-driven Requirements Adaptationmentioning

confidence: 99%

Won't Take No for an Answer: Resource-Driven Requirements Adaptation

Bennaceur

Zisman

McCormick

et al. 2019

2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Adaptive composition dynamically and opportunistically uses and combines resources to best satisfy user requirements. However, when available resources cannot satisfy those requirements, no guidance or alternative options are offered by existing composition solutions. In this paper we address this issue by presenting an approach that tries to find substitutions for unavailable resources while satisfying the initial requirements. If no satisfactory substitutions are found, the requirements are adapted based on the resources available. Given that such requirements adaptation might be unbounded, we limit the search space guided by the available resources. Our approach ensures that alternative compositions given to users are achievable using available resources. We demonstrate the validity of our approach by implementing a prototype tool and applying it to support individuals in meal planning to reduce food waste.

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Yum-Me

Cited by 119 publications

References 57 publications

Health Recommender Systems: Systematic Review

Health Recommender Systems: Systematic Review

Nudging Healthy Choices in Food Search Through Visual Attractiveness

Won't Take No for an Answer: Resource-Driven Requirements Adaptation

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