This study presents the results of two studies where we compared the effectiveness of 8 different justification strategies across 3 styles for supporting healthier recipe choices

1. “Tell Me Why”: using NLP
justifications in a recipe
recommender system to support
healthier food choices
Work with Cataldo Musto, Amon Rapp,
Christoph Trattner & Giovanni
Semeraro
a.d.starke@uva.nl
Dr.ir. Alain Starke
09-02-2024

4. Online recipes & food recommender systems
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(This is a content-based recommendation)

5. • Many users do not log in  Popularity-based strategies
• Popular recipes tend be unhealthier
• Content-based methods (most popular) also do not diversify much
To support healthy eating,
recipe websites can be nothing but a heartbreak
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6. Different method of personalization/tailoring:
Knowledge-based recommendation
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Create a custom scoring
system for recipes
• User characteristics relate
to recipe features

7. My one desire: Healthier Choices
My fire: Recommender explanations / justifications
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8. Natural Language Processing Pipeline
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9. • To what extent can different justification styles and strategies support
healthier recipe choices (compared to choosing the popular one)?
• Can self-reported motivations predict these choices?
RQs
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10. • 4,671 Mediterranean-style recipes (not all used)
• Various features: allergies, nutrients, costs, difficulty, prep. time
Lunch might feel two worlds apart, but here’s
the Recipe Dataset: Giallo Zaferano
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11. Three explanation / justification ‘styles’
• No Justification
• Single Justification (describing recipe features separately)
• Pairwise Justification (comparing recipe features, e.g. ‘higher than’)
• 8 types of justification strategies (i.e., popularity, food features, etc.)
• N=504
Study Design: 3 conditions
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12. Interface (with two strategies)
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13. Results
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14. Results – to predict healthy choices
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15. • Similar setup, but users could change an initial choice
• Results were similar for N=504
Second Study
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16. • Focusing on healthy foods only would lead to
dissatisfaction, but justifications (an informative nudge)
could be used to retain freedom of choice
• Supporting users with specific goals / motivations
• Effectiveness of a recommender method may depend on a
person’s literacy levels
Some Thoughts
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