=Paper= {{Paper |id=Vol-1388/latebreaking_paper4 |storemode=property |title=What's On My Plate: Towards Recommending Recipe Variations for Diabetes Patients |pdfUrl=https://ceur-ws.org/Vol-1388/latebreaking_paper4.pdf |volume=Vol-1388 |dblpUrl=https://dblp.org/rec/conf/um/RokickiHD15 }} ==What's On My Plate: Towards Recommending Recipe Variations for Diabetes Patients== https://ceur-ws.org/Vol-1388/latebreaking_paper4.pdf

What’s On My Plate: Towards Recommending
Recipe Variations for Diabetes Patients

Markus Rokicki, Eelco Herder, Elena Demidova

L3S Research Center, Hannover, Germany
{rokicki,herder,demidova}@L3S.de

Abstract. As community-based recipe platforms continue to grow in
popularity, recipe recommendation is an active research area. Simulta-
neously, the analysis of online recipes can provide us with insights on
dietary patterns in particular communities. In this paper, we focus on
recipe recommendation for a user group that is constrained in terms of
choices: diabetes patients need to balance their diet more than average
persons and to be aware of the nutritional value of their meals. First,
we discuss the type of situations where diabetes-specific food recommen-
dations are desirable. Further, we analyze how people’s age and gender
interact with food intake. Based on a large dataset, we explore how vari-
ations in ‘canonical meals’ can be exploited for recommending which
alternatives better fit the user’s dietary requirements.

1 Introduction

Diabetes is a widely spread chronic disease that affects about 10% of the Western
population. Patients suffering from diabetes have to take many vital decisions
on a daily basis, including: am I allowed to eat this, what is my blood sugar
level, how much insulin should I take right now? Particularly those who have
just been diagnosed with diabetes experience difficulties facing such decisions.
The GlycoRec project1 aims to develop a system that provides diabetes pa-
tients with personalized support and advices for improving their everyday lives.
GlycoRec will provide patients with information and advice regarding their nu-
trition, physical activities, and the use of medicine. This empowers patients to
better communicate their needs with their doctors and advisors, and to better
implement advices and stated goals in their everyday lives.
In this paper, we present our first steps towards personalized nutritional ad-
vice. Despite the availability of online nutritional information2 , these databases
only provide information on single ingredients and/or standardized products
such as ready-made meals. Our goal is to provide diabetes patients with rec-
ommendations and feedback in everyday situations, including: (i) How many
1
https://www.pfh.de/hochschule/forschung/forschungsprojekt-glycorec.html
2
The quality and acceptance of databases with nutritional information vary wildly.
In Germany, an established resource is http://www.mri.bund.de/de/service/
datenbanken/bundeslebensmittelschluessel.html.
carbohydrates can I expect that the Thai curry on the menu contains?, and (ii)
Which recipe variation best matches both my dietary restrictions and my taste?
Based on an extensive dataset from the German recipe website Kochbar.de3 ,
we show that there are differences in eating patterns with respect to gender, age
group, and dietary restrictions, such as diabetes. We cluster popular meal names
into ‘canonical meals’ and analyze to what extent these meals vary in terms of
ingredients and nutritional value. These insights provide directions for strategies
to find the best recipe or for adapting recipes to user needs and preferences.

2 Related Work

Diabetes mellitus is a widespread disease that requires constant attention of
the patients and their caregivers. Research has shown that effective prevention
of diabetes-related complications includes lifestyle changes that include an in-
creased physical activity as well as a diet that is associated with lower blood
pressure [6]. Telemedicine and the improved acceptance of smart phones and
tablet computers among patients and physicians, contributes to improved patient
guidance and self-empowerment [7]. A particular focus of guidance is nutrition.
Online recipe recommenders can play an important role in generating healthy
meal plans. Even though the used ingredients are the major reason for liking or
disliking a meal, there are health-conscious users who also take nutritional infor-
mation into account [3]. In a feasibility study on recipe recommendation, Freyne
and Berkovsky found that both content-based (e.g. ingredients) and collabora-
tive approaches (taste, context) should be taken into account [2].
An in-depth analysis on how users choose and adapt recipes is given by Teng
et al [8]. Making use of complement and substitution networks, they show which
ingredients users add, remove, pair or substitute. This allows them to predict
which variation of a recipe will receive the best ratings.
Kusmierczyk et al. analyzed data from the German community platform
Kochbar and found clear seasonal and weekly trends in online food recipe pro-
duction, both in terms of nutritional value (fat, proteins, carbohydrates and calo-
ries) [5] and in terms of ingredient combinations and experimentation [4]. West
et al. [9] analyzed similar patterns for the American population, with slightly
different results. They were also able to automatically detect anomalous days -
bank holidays and other celebrations - and users who aimed to change their diet.
Making use of these and other insights, the team from IBM Watson created
the prototype Chef Watson, which automatically creates recipes that match user
preferences, based on existing recipes from the Bon Appetit recipe website [1].

3 Dataset and Preprocessing

We use a crawl from Kochbar.de, a German online food community website to
which users can upload and rate cooking recipes, provided by Kusmierczyk et
3
https://www.kochbar.de
al. [4]. The dataset encompasses more than 400 thousand recipes published be-
tween 2008 and 2014. In addition to information on ingredients and preparation,
more than 330 thousand recipes also contain nutrition facts. Almost 200 thou-
sand users provided more than 2.7 million comments and 7.7 million ratings.
The ratings are on a Likert scale, but - surprisingly - they are overwhelmingly
positive (99.1% gave a rating of 5).
We consider only the 309 thousand recipes that contain valid information
on energy (in kJ and kcal), carbohydrates, proteins, and fat. For each recipe,
a mostly structured list of ingredients (with quantities) is given. We extracted
the ingredients and performed simple normalization by converting the text to
lowercase, normalizing whitespaces, removing text in parentheses, and splitting
on conjunctions such as “and” and “or”. This process yields more than 300
thousand ingredients, with an average of 10 ingredients per recipe. As only a
moderate amount of 2258 ingredients occurs frequently (in at least 100 recipes),
this simple preprocessing is sufficient for a first analysis of the dataset.

4 Differences in Food Intake

As a first step, we aim to identify differences in user recipes created by different
user groups. Among the 200 thousand users, 95 thousand provided information
regarding gender (25 thousand male, 70 thousand female users) and 57 thousand
provided information regarding their age (mean 42.2, median 42). In addition,
we are interested in diabetes patients. However, apparently most patients did not
disclose the fact that they suffer from diabetes: from the profile information we
are able to identify only 65 users who suffer from diabetes or have close relatives
with diabetes - a number too small for an effective analysis. Similarly, only about
3.000 of the recipes have been labeled as ‘diabetes friendly’. By contrast, about
220.000 recipes are marked as gluten free, 137.000 as lactose free and (only)
37.000 recipes as vegetarian.

30 carbohydrates
fat
proteins
Average Nutrients [g / 100g]

[10, 20) [20,30) [30, 40) [40, 50) [50, 60) [60, 70) [70, 80]
User Age

Fig. 1: Average nutritional facts of recipes for different user ages as given by the
recipe authors.
Figure 1 shows the average nutritional values of recipes created by different
age groups. The levels of fat and protein are quite stable between age groups,
but the amount of carbohydrates consistently decreased with age (F = 152,
p < 0.01). Reduction of carbohydrates is a recommendation provided by most
nutrition advice centers.
Figure 2 shows the average nutritional values of the recipes provided by dif-
ferent user groups. The most noticeable difference can be observed for carbohy-
drates: recipes provided by female users are, on average, richer in carbohydrates
than recipes provided by male users (t=52.108, p < 0.01). As most users in
Kochbar are female, this is reflected in the averages for all users. There are two
explanations for this effect: first, baking recipes are typically written by women;
Second, male users are on average older (50.9 years) than the female users (43.8
years) in our dataset (t=5.629, p < 0.01) - as mentioned earlier, the average
intake of carbohydrates decreases with age (Figure 1).
The two rightmost bars in Figure 2 correspond to the recipes provided by
self-reported diabetes patients and recipes that are marked as diabetes friendly.
Recipes of the self-reported diabetes patients are clearly lower in fat (t=2.991,
p < 0.01) and contain more protein than recipes from other users (t = 5.629,
p < 0.01) - which is in line with recommendations from diabetes information
centers. By contrast, recipes marked as ‘diabetes friendly’ seem not to differ
from regular recipes.

22
all
20 male
female
Average Nutrients [g / 100g]

18 diabetes patients
diabetes category 300
Energy [kcal / 100g]

16
250
14
200
12
150
10
100
8

6 50

4
carbs fat proteins kcal
Nutrition Information

Fig. 2: Average nutritional facts of recipes for different user groups and for the
recipes assigned to the category “diabetes”.

5 Canonical Meals and their Variations
As discussed by [2], most users do not search for recipes based on their nutritional
values, but rather look for recipes with certain ingredients or for a particular
dish. As is to be expected, many variations of popular recipes can be found
at Kochbar.de. In order to find out differences in nutritional value within a
particular type of dish, we selected the 200 most frequently used recipe titles as
‘canonical meals’, to which we assigned all recipes of which the title contained
the title of the canonical meal. The ‘top meals’ are shown in Table 1. From the
selection one can clearly see that the user base of Kochbar.de is German.

Canonical Meal Recipes Ratings Comments
Kartoffelsalat (potato salad) 1,863 41,226 14,117
Pizza 1,812 38,250 13,210
Käsekuchen (cheese cake) 1,681 36,807 12,085
Apfelkuchen (apple pie) 1,450 34,935 11,982
Gulasch (gulash) 1,187 28,668 10,162
Nudelsalat (pasta salad) 1,706 30,196 9,782
Eierlikör (egg liquor) 1,085 27,482 9,360
Pfannkuchen (pancake) 1,221 24,492 8,550
Lasagne (lasagna) 1,187 22,570 8,034
Tiramisu 1,358 22,931 7,773

Table 1: Canonical meals of which the recipes received most comments.

To find out to what extent canonical meals vary in nutritional value, we se-
lected three different, representative meals and calculated the means and stan-
dard deviations - see Table 2. We also analyzed which ingredients are associated
with recipes that are high and low in carbohydrates, fat, proteins, and energy -
by calculating the average levels for all meals and sorting them accordingly.
Potato salad is low in protein, but the standard deviations are relatively high.
Ingredients associated with high protein are meat and fish, low-protein recipes
contain vegetables instead, such as pickles, radish, olives and asparagus. The
same pattern can be found for lasagna. Low-fat cheese cake is associated with
low-fat milk products and high-fat with chocolate and cream cheese.

Carbohydrates Fat Proteins Kcal
Meal Mean Std dev Mean Std dev Mean Std dev Mean Std dev
Potato salad 10.26 8.025 13.09 17.64 3.27 3.60 171.25 151.17
Cheese cake 29.93 15.68 11.97 9.29 7.32 2.97 256.29 95.96
Lasagna 8.44 10.81 13.23 11.00 8.06 6.18 185.43 137.25

Table 2: Nutritional facts for 3 popular canonical meals.

These findings confirm our expectation that it is possible to estimate the
nutritional value of certain dishes from the recipes associated with these dishes
and that one can identify ingredients associated with high or low levels of food
value. This provides a good base for developing food recommender systems that
suggest ‘better recipes’ and alternative ingredients for particular recipes.
6 Discussion and Outlook
Existing food databases for diabetes patients are incomplete and inconsistent.
Moreover, they only contain ingredients and standardized products. Diabetes
patients typically learn over time what they can eat and what not, but this is
often not sufficient for many common situations. In restaurants, served meals are
often ‘black boxes’ in terms of nutritional value, which causes great uncertainty
among diabetes patients, especially when trying out new meals while on vacation
in foreign countries.
To provide patients with advice and information on the nutritional value of
a meal - and to recommend them alternative meals or ingredients - we aim to
exploit recipe sites such as Kochbar.de, which contain user-provided recipes. This
paper provides some first insights and confirms the feasibility of the approach.
A particular challenge for the food recommender will be to provide detailed
feedback on the precision of its estimations and the resulting recommendations.
Particularly for ‘canonical meals’ with many variations, estimations may need
to be refined with additional user input and feedback.

Acknowledgment
The GlycoRec project is funded by the Federal Ministry of Education and Re-
search (BMBF) under the funding scheme Adaptive, Learning Systems (Adap-
tive, lernende Systeme).

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