Exploring the Effects of Natural Language Justifications in Food Recommender Systems

CATALDO MUSTO*, ALAIN STARKE#,^, CHRISTOPH TRATTNER^, AMON RAPP°, GIOVANNI SEMERARO*
(*) UNIVERSITÀ DEGLI STUDI DI BARI ‘ALDO MORO’ – ITALY (#) WAGENINGEN UNIVERSITY & RESEARCH – THE NETHERLANDS
(^) UNIVERSITY OF BERGEN – NORWAY (°) UNIVERSITY OF TORINO - ITALY
Exploring the Effects of
Natural Language Justifications
in Food Recommender Systems
ACM UMAP 2021 – 29th Int. Conf. on User Modeling, Adaptation and Personalization - Online from Utrecht - June 23, 2021

Background
Cataldo Musto, Alain Starke, Christoph Trattner, Amon Rapp, Giovanni Semeraro
Exploring the Effects of Natural Language Justifications in Food Recommender Systems. UMAP 2021 – Online – June 23, 2021

Health-aware Food RecSys
?
Goal: to identify - among a set of suitable suggestions -
the healthiest recipes for a target user

Goal: to identify - among a set of suitable suggestions -
the healthiest recipes for a target user

!
Problem: users tend to prefer
popular (and unhealthy) recipes (^) (*)
(^) Christoph Trattner and David Elsweiler. 2017. Investigating the
healthiness of internet-sourced recipes: implications for meal planning
and recommender systems. In Proceedings of the 26th international
conference on world wide web. 489–498.
(*) Cataldo Musto , Christoph Trattner, Alain Starke, Giovanni Semeraro:
Towards a Knowledge-aware Food Recommender System
Exploiting Holistic User Models. UMAP 2020: 333-337

How can we encourage
people to make
healthier food choices?
RESEARCH PROBLEM

Contribution
▪We design a NLP pipeline that automatically
generates Natural Language Justifications
▪Instead of just showing a recommendation, we also
generate a justification supporting the suggestion
▪ Our conjecture
▪More informed users make healthier food
choices

Use Case
«Vegetable Soup has less calories and more proteins than spaghetti cacio and
pepper. Moreover, spaghetti cacio and pepper has more satured fats.
To intake many saturated fats increases the risk of heart diseases.
Given your high BMI, you should consider this.»

Methodology

Our Workflow

Step 1: PROFILER

Step 1: PROFILER
Demographics
Affect
Behavioral Data
Health Data
Domain Knowledge
Preferences and Goals
Mood (positive, negative, neutral)
Level of Physical Activity
Cooking Experience, Available Time,
Cost Constraints
Lifestyle, Amount of Sleep, Stress Level, BMI
Gender, Age, Weight, Height
Food Preferences and Restrictions
Personal Goals (e.g., Losing Weight)

Step 2: RECIPE ANALYZER

Step 2: RECIPE ANALYZER
++ +++
++
+ ++
Input
Output
One or two recipes
Information about the recipes (amount of macro-nutrients or a comparison)

Step 3: GENERATOR

Step 3: FOOD KNOWLEDGE BASE

Step 3: FOOD KNOWLEDGE BASE
Encodes common-sense and general knowledge about food consumption, risks and benefits
For each macro-nutrient, around 10 facts are encoded in the knowledge base. 150 in total.
HIGH HIGH
STROKE
RISK
SATURATED
FATS
HIGH HIGH
PRESSURE
RISK
SODIUM
HIGH HIGH
MUSCLE
DEVELOPMENT
PROTEINS
HIGH
SUGAR
IMPROVES
MOOD

Workflow Recap
vs.

How are the
justifications generated?

Justification Styles and Strategies
▪Based on the principles of Natural Language Generation
▪All the justifications are dynamically built by our workflow
▪Template-based structure, filled in based on user’s features and recipes
characteristics
Two different justification styles
Single Explains the features of each recipe separately
Comparative Provides a comparison of the two recipes
Eight justification strategies
Each justification strategy highlights and emphasizes a different aspect of the
recommended recipe (e.g., nutrients, popularity, healthiness, health risks and
benefits, etc.)

▪ All the justifications are dynamically built by our workflow
▪ Template-based structure, filled in based on user’s features and recipes characteristics
▪Two different justification styles
▪ Single Explains the features of each recipe separately
▪ Comparative Provides a comparison of the two recipes
Eight justification strategies
Each justification strategy highlights and emphasizes a different aspect of the
recommended recipe (e.g., nutrients, popularity, healthiness, health risks and benefits,
etc.)

▪ All the justifications are dynamically built by our workflow
▪ Template-based structure, filled in based on user’s features and recipes characteristics
▪Two different justification styles
▪ Single Explains the features of each recipe separately
▪ Comparative Provides a comparison of the two recipes
▪ Eight justification strategies
▪ Each justification strategy highlights and emphasizes a different aspect of the
recommended recipe (e.g., nutrients, popularity, healthiness, health risks and benefits,
etc.)

Overview of the Strategies:
Description and Popularity
▪ ‘Baseline’ justification strategies
▪ Description Provides a textual description of the recipe
▪ Popularity Shows how popular is the recipe
Spaghetti Cacio and Pepper are one
of the dishes of the Roman Tradition:
grated pecorino and peppercorns,
a quick and tasty recipe.
Vegetable Soup is a genuine and
healthy dish, a perfect winter
comfort food
Spaghetti cacio and pepper is more
popular than Vegetable Soup
in the community
vs.
DESCRIPTION – SINGLE STYLE POPULARITY – COMPARATIVE STYLE

Overview of the Strategies:
User’s Skills, User Goals, User Lifestyle
▪ Link user’s information to the characteristics of the recipe
▪ User Features: User’s cooking skills, self-set goals, personal lifestyle
▪ Recipe Features: difficulty, calories and healthiness of the recipe
vs.
USER SKILLS – SINGLE STYLE
Spaghetti cacio and pepper
has more calories than
Vegetable Soup (491 vs.
462). Vegetable soup can
better help to reach your goal
of losing weight.
z
Spaghetti Cacio and Paper has a medium
level of difficulty. It might not be adequate to
your cooking skills, which are low.
Vegetable Soup is very easy to prepare. The
recipe seems adequate to your cooking
skills, which are low.
USER GOALS – COMPARATIVE STYLE

Overview of the Strategies
Food Features, Health Risks and Benefits
▪Highlight distinctive characteristics of the recipes and links information
about macro-nutrients to health risks (or benefits, respectively)
▪Input Features: macro-nutrients, user characteristics (BMI, mood, sleep, stress, etc.), facts
from food knowledge
vs.
Spaghetti Cacio and Pepper has a higher amount of saturated fats
(8.7gr vs. 4.55gr) and a lower amount of fibers (4.55gr vs. 7.3gr)
than Vegetable Soup.
To intake many saturated fats increases the risk of heart diseases.
Given your high BMI, you should take into account this fact. On the
other side, to intake many fibers increases the risk of constipation.
HEALTH RISKS – COMPARATIVE STYLE

Experimental
Evaluation
Do natural language justifications affect user choices for healthy recipe
recommendations, compared to popular ones?

Method
• 4,671 Mediterranean-style recipes
• 503 participants (Amazon MTurk) completed the study:
1. Disclosed personal information
2. Chose 3 x 1 recipe from a pair (healthy vs popular): a first course, second course, and a dessert
• Recipe pairs were subject to 3 between-user conditions, presented with either:
◦ No Justifications (baseline)
◦ Single-style Justifications
◦ Comparative-style Justifications
• For each pair, we randomly used one of the eight justification strategies.

Interface
Justification
Choice
Motivation

Results

Recipe Choices Across Conditions
and Meal Types
→ % of Healthy Choices was higher for comparative justifications, compared to the ‘no
justification baseline’.
→ No such effects for single justifications

Effectiveness of Specific Strategies
Across all meal types:
◦ No strategy decreased the likelihood of choosing a healthy recipe
◦ Food Features and Health Risks Strategies increased the likelihood

Why Did Users Choose Healthy Recipes?
Analysis of Choice Motivation
Logistic regression analyses, predicting healthy recipe choices using all choice motivations
Clearest effects across all meal types:
◦ Taste was negatively related to reasons for choosing a healthy recipe
◦ Health was positively related to choosing healthy recipes

Concluding Remarks

Conclusions and Future Work
•Our NLP pipeline captures: Eating preferences (User) and features (Recipes), to generate
healthy recipe advice, with justifications.
• Comparative Justifications can support user goals, on top of personalized recommendations
• Our healthy Recs are founded in a user’s choice motivation
• Future work: Can recommender support changes in eating habits, by combining *what*
should be recommended with *how*?

Questions are welcome!
cataldo.musto@uniba.it, alain.starke@wur.nl
@cataldomusto, @alainstarke
Contacts

Exploring the Effects of Natural Language Justifications in Food Recommender Systems

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