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Bergen Open Research Archive - UiB
Article . 2024 . Peer-reviewed
License: CC BY
DBLP
Conference object . 2024
Data sources: DBLP
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Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems.

Authors: el Majjodi, Ayoub; Khan, Sohail Ahmed; Starke, Alain Dominique; Elahi, Mehdi; Trattner, Christoph;

Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems.

Abstract

The visual representation of food on digital platforms affects the foods chosen by users, including in the context of recommender systems. Previous studies show that small changes in visual features can influence human decision-making, regardless of whether the food is healthy. This paper reports on a study aimed at better understanding how users perceive the attractiveness of food recipe images in the digital world. In an online mixed-methods survey (N = 192), users provided visual attractiveness ratings of food images on a 7-point scale, along with textual assessments. We found robust correlations between fundamental visual features (e.g., contrast, colorfulness) and perceived image attractiveness. The analysis also revealed that, among other user factors, cooking skills positively affected perceived image attractiveness. Regarding food image dimensions, appearance and perceived healthiness were significantly correlated with user ratings of food image attractiveness.

Countries
Netherlands, Norway
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Keywords

:Theoretical computer science, programming science and theory: 421 [VDP], :Teoretisk databehandling, programmeringsspråk og -teori: 421 [VDP]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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