Multimodal Analysis of User-recipes Interactions
Emilija Georgievska
, Martina Stojanoska
, Sanja Mishovska
Tome Eftimov
and Dimitar Trajanov
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Republic of North Macedonia
Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia
Keywords: Food, Recipes, Interactions, Recommendations.
Abstract: A good diet is essential for good health and nutrition, but also as a way of expressing and feeling good.
Culinary and food recommender systems are becoming increasingly popular at a time when people are facing
fast-paced lifestyles. In this paper, we are analysing interactions between users and recipes in order to make
food recommendations based on their previous behaviour which would result in higher personalization for
every single person. This also raises the question of whether people stick to what they know well or are open
to new suggestions, or do personal recommendations lead to more homogeneity.
As technology advances and succeeds to rise above
its peak in plenty of industries, there is still space for
a big breakthrough in the food industry. For sure, it is
great to have a digital menu on your phone with a
single scan of a QR code, but what about food
recommendation systems? A recommendation
system with the possibility of generating good food
suggestions can revolutionize not only the food
industry but also people’s lives.
However, the food domain is not straightforward
or uncomplicated since it revolves around people and
their unique characteristics. To be successful, one
recommendation system needs to deal with numerous
features such as recipes, food ingredients, and their
mutual combination, nutritional values, and cooking
Following the factors that should be addressed
and the features that should be taken into
consideration, it is vastly complex to build a
recommendation system that does it all. There are
numerous types of food recommendation systems,
each with its features and focusing on different
problems. For instance, a recommender system that
focuses on the person’s behaviour and the food
What additionally complicates the development
of recommender systems is the concept of filter
bubbles. The term filter bubble suggests a situation
that means that every user on the Internet exists in its
universe of information where we receive
information based on our behaviour, knowledge, and
activities, neglecting the diversity of information that
we should be receiving (Bruns, 2019). Even in
environments where there are no recommender
systems, a large portion of the users tend to repeat
their eating patterns and habits. The presence of
recommender systems especially ones that are based
on previous user’s behaviour can furthermore lead to
homogenization (Aridor et al., 2020). The question is
while developing recommender systems should we
try to burst the bubble or give the user what he already
knows and likes (Amrollahi, 2019).
The whole idea behind recommender systems and
recommendations themselves lies behind the desire to
help people by assisting them in the decision-making
process and overcoming the load of available
Georgievska, E., Stojanoska, M., Mishovska, S., Eftimov, T. and Trajanov, D.
Multimodal Analysis of User-recipes Interactions.
DOI: 10.5220/0010902800003123
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 689-696
ISBN: 978-989-758-552-4; ISSN: 2184-4305
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
information (Trattner and Elsweiler, 2017). As in
everyday life, they are also a helping hand in e-
commerce and in classic commerce so the businesses
can bring their products as close as possible to the
needs of their customers (Schafer et al., 1999).
Recommender systems in the food industry are
becoming more relevant over the years and the
demand for them is increasing (Trattner and
Elsweiler, 2017).
In this paper, the focus is put on a single person’s
lifestyle and eating habits. By using multimodal
analysis of the users-recipes interactions the
recommendation models will be able to predict
possible future food choices, and with comparison
between the two, we will be able to tell if the results
acquired are similar or a complete match.
With the ubiquity of the Internet, individuals tend
to share everything in their lives nowadays, and food
recipes are not an exception. Currently, there are
many cooking websites, few clicks away from us on
the Internet, which provide various recipes and food
content (e.g., description, meal photos, cooking
videos, how-to guides), as well as useful functions for
searching and filtering.
The dataset used in this research is data from, publicly available on Kaggle (Li, 2019).
Following the data, the main goal is to build a
system that can efficiently predict which recipe can
be of interest to a user by learning about the user’s
past choices and preferences and also adding diversity
to the recommendations This would minimize user’s
efforts to search through enormous databases of
recipes on websites, only to find ones that are not to
their liking.
Recommender systems have a core function to
recommend items that the user would actually take
into consideration (Ricci et al., 2011), with content
filtered recommendations that consider the user’s past
choices and preferences, or collaboratively filtered
recommendations that consider similar users and their
choices and interests (Shokeen and Rana, 2020).
Some researchers say that to give good food
recommendations, it is necessary to take into
consideration the quantity of the ingredients and also
the specificity of the ingredients in the recipes that the
user browsed or cooked (Ueda et al., 2014). Given
this, the algorithms often find similar recipes based
on overlapping ingredients, either treating each
ingredient equally or by identifying key ingredients.
(Geleijnse et al., 2011) built a version of a
personalized recipe recommendation system that
suggests recipes to users based on past food choices
and nutrition intake.
Though there are several choices, to our
knowledge, we chose a hybrid, multimodal approach,
which connects the previous history of recipe usages
for the user and also the ingredients contained, in
order to result in recommendations that are familiar
to the user, but also recommendations that
incorporate new various ingredients. Therefore, in
this paper, we do our first attempt to investigate how
good the recommendations can be if they are based
on the user's previous experience or as we like to say
food choices and preferences and the ingredients in
those food choices.
But in order to map the whole user’s history into
one piece, a specific approach is needed. If
interactions are mapped to a bipartite user-recipe
interaction graph, a recommendation problem can be
transformed into a link prediction problem (Li and
Chen, 2009). The constructed bipartite graph can
capture important information on the relationship
between the users and case recipes (Li and Chen,
2009). However, a weighted network is much more
informative than an unweighted one, so a lot of
techniques can be applied to determine the link's
weights (Zhou et al., 2007).
Even though low-dimensional node embeddings
in large graphs have been proven as useful in link
prediction problems as this one, a big deal of the
approaches require all of the nodes to be present
during embeddings training (Hamilton et al., 2017).
However, due to the low generalization to unseen
nodes of these approaches, they do not seem fit to
recommend problems. GraphSAGE and his
heterogeneous version HinSAGE, efficiently
generate node embeddings for previously unseen
data, where instead of training individual embeddings
for each node, a function that generates embeddings
by looking into the node’s local neighborhood is
learned (Hamilton et al., 2017).
On the other side of the story, in the last few years,
many meta-path-based algorithms are proposed to
carry out data mining tasks over HINs, including
similarity search, personalized recommendation, and
object clustering. In particular, the concept of meta-
paths, which connect two nodes through a sequence
of relations between node types, is widely used to
exploit rich semantics in HINs. Following the
example of the metapaths and their meaning, in this
particular research the metapath P1: U R U, will
mean that two users have cooked the same recipe,
whereas the metapath P2: R U R, will mean that
HEALTHINF 2022 - 15th International Conference on Health Informatics
a user has cooked two of the recipes. Metapath2Vec
is proven to capture better proximity properties,
semantics between different types of nodes and learn
better than other the algorithms which abandon
different node and edge types (Zhang et al., 2018).
The data is crawled from which is one of
the largest online recipe sharing communities,
covering 18 years of activity (January 2000 to
December 2018).
There are three key parts in the data: recipes,
users, and interactions between them. For every
recipe there is detailed information such as the
ingredients that we need to prepare it, preparation
directions, categories added by users, and also the
nutrition facts of the given recipe. Also, for every user
there is information about the cooking techniques
encountered by the user, recipes interacted with and
rating to the recipes retrospectively.
The raw data consists of more than 180 thousand
recipes and 700 thousand user-recipe interactions
(reviews and ratings).
One very mitigating circumstance is that the data
mainly comes in 2 different formats, where the first
part of the data-sets are more natural with descriptive
textual attributes in natural English language and
other languages, that we use to understand and
analyse the data. The second part of the data-sets are
the same data-sets as the first, but pre-processed,
without missing values, and encoded textual
attributes to numeric attributes. These include
separate pre-processed data-sets for training, testing
and validating. This makes it very easy to use the pre-
processed data-sets directly as an input to algorithms.
All of the data-sets consist of comma-separated
After analysing each one of the data-sets, there are
some interesting facts about the overall data: 231.637
recipes were published by 27.926 users, and most
recipes were published by the user with id: 89831
(3118 recipes). The average steps for making one
recipe are 9 steps, and there is a recipe with 145 steps.
The average ingredients for making a recipe are also
9 ingredients. Recipes that contain more than 25
ingredients are very rare. It is also shown that over the
years, users tend to change their lifestyle and interact
with healthier recipes.
Using a multimodal approach, the first aspect that is
being considered is the temporal dimension of the
data with the help of the StellarGraph Python library
and the HinSAGE algorithm which is a heterogenous
extension of the GraphSAGE algorithm (CSIRO's
Data61, 2018).
The goal here is to predict if the interactions that
we know really occurred. For that purpose, the data is
divided in two parts using the first half of the data for
training and the second half of the data for testing the
The second aspect focuses on future recipe
recommendations, instead of the ones that already
happened. First of all, we obtain recommendations
with similar ingredients based on text similarity, and
then recommendations based on the graph structure
with MetaPath2Vec (Dong et al., 2017).
4.1 Temporal Interaction Prediction
Since the main goal revolves around personal
recommendations, it is crucial to modify the data in a
way that the information within is captured correctly
and efficiently.
The nature of the data implies that there are two
distinct entities - users and recipes which are mutually
connected by interactions. The interaction can be a
rating or a review of a particular recipe which is
preceded by trying the same recipe. However,
although the users and recipes are connected, it is not
naturally implied that the users are mutually
connected or the recipes are mutually connected.
Because of this structure, a user-recipe network can
be created as a complex network which displays a
natural bipartite structure.
A bipartite graph is a graph whose vertices can be
split in two independent sets, where every edge
connects one vertex from the first set and one vertex
from the second set (Guillaume and Latapy, 2004).
Following the particular data, a bipartite graph
with two types of nodes (users and recipes) and edges
that correspond to user-recipe interaction can be
constructed. Besides that, a proper weighting method
is required so that the original information is captured
and retained (Zhou et al., 2007). All of the links have
the default weight of 1, which suggests that a link is
existent (note that if a link weights 0, it is not present
in the graph). Weighing the links by their existence
contributes more to the link-prediction problem in
graphs than weighing the links (note that a link is an
interaction) by the rating.
Multimodal Analysis of User-recipes Interactions
The first step towards constructing some kind of
structure that can be used for predictions was trying
out the NetworkX library X (Hagberg et al., 2008).
NetworkX is a Python software package that is used
to study, create and manipulate large complex
networks that may be represented in the form of
graphs, like this case. Adjacency matrix was created
with users on one side and recipes on the other side,
with a 1 on places where interaction took place, and 0
where it did not. This matrix later was fed into a
NetworkX graph. One drawback was that the matrix
was very sparse, and it required great computational
power for processing. Another drawback was that this
structure was successful only to the point where
predicting algorithms ought to be used. Many state-
of-the-art algorithms require a specific kind of graph
as an input, so they can thoroughly learn about the
graph and the relations it has.
The most suitable structures and state-of-the-art
prediction algorithms for building bipartite graphs
and link prediction problems are part of the
StellarGraph library, which offers an easy way to
discover and learn the patterns in the data and guide
towards good recommendations.
StellarGraph as a class for graph machine learning
offers an easy way for storing graph structure by
providing collection of nodes and collection of edges
which connect a source node to a target node. In order
to achieve and preserve the heterogeneity of the data,
a heterogeneous undirected StellarGraph is defined,
with users as the first and recipes as the second type
of the nodes.
Given a bipartite user-item interaction graph, we
can convert the recommendation problem in a link
prediction problem (Li and Chen, 2009).
Considering that, with the help of HinSAGE,
supervised link prediction is the next step towards
building a recommendation system. HinSAGE
supports representation learning, node
classification/regression and link
prediction/regression as the original GraphSAGE but
for heterogeneous graphs. Link prediction is one of
the most important research topics in the field of
graphs and networks. The primary goal of link
prediction is to discover pairs of nodes which are
likely to form a link in the future. Retrospectively, in
this research paper, given a pair of nodes, the goal is
to identify whether a link would or would not exist in
the future. But how can solving link-prediction
problems be of a service to the construction of
recommendation systems? Given the
recommendations obtained from the next section, the
HinSAGE model could predict the probability of a
link between a particular user and the
recommendation suggested.
The model has the following architecture: two-
layer HinSAGE model that takes labeled (user,
recipe) node pairs that correspond to a particular user-
recipe interaction and output a pair of node
embeddings for the user and recipe nodes. Then these
embeddings are fed into a link regression layer, which
concatenates them and turns them into a link
embedding. The newly constructed link embeddings
are passed through a link regression layer to obtain
the probability of a certain user-recipe node pair
existence. The entire model is trained end-to-end by
minimizing the root mean square error as the loss
function. The model’s ability to predict a certain user-
recipe interaction lies behind the manipulation of the
link’s weights and therefore simplifying the problem
as a binary classification problem.
As said before, all the existent links in the graph
have the default weight of 1. To balance the model,
non-existent links were generated and weight of 0
was given to them (a link between a user and recipe
has weight of 0 only if there was no interaction
between the pair). To treat the problem as a
classification problem, one more thing was done. The
results from the link regression layer were rounded
with a threshold of 0.5. If the result is below 0.5
there is no significant evidence that shows that there
is or will be an interaction between a pair of a user
and a recipe. Likewise, if the result is above 0.5 or 0.5
there is evidence that leads the model to believe that
there is or will be an interaction between a pair of a
user and a recipe.
Following this principle, all of the known
interactions were divided by years, taking the first ten
years for training and the rest for testing (following
the 70:30 rule). Figure 1 shows that interactions are at
their peak in 2008 and thereafter begin to drop, owing
to people sharing less information and interacting
Figure 1: Interactions between users and recipes by years.
HEALTHINF 2022 - 15th International Conference on Health Informatics
with previously submitted recipes over time. This was
done to research more about the temporal ability of
graphs and how they change over time.
It is obvious that some people’s preferences and
choices change over time, along with the trend of a
healthier life that has been more present over the past
few years. All of this is visualized in Figure 2, from
which we can see how the consumption of olive oil is
increasing year after year.
Figure 2: Cooking Oil usage through the years.
By testing with the rest 30% of the data (from 2011
to 2018), the model had an accuracy of 0.828. The
accuracy led to a conclusion that the model gives
good results based on a user’s previous behaviour,
given the fact that over time people’s taste can
change. Given the model which can predict if there is
or will be an interaction between a user and a recipe
with an accuracy of nearly 0.828 shown on Figure 3,
the next part of the research is to find the right
algorithm for recipe recommendations. Then using
the model, we can test if the corresponding user
would take the recipe recommendation in
consideration. This can be done by testing if the
model predicts an interaction between the pair of user
and recipe in consideration.
Figure 3: Prediction if a future interaction will happen.
4.2 Future Recipe Recommendation
This aspect is completely different from the first one,
and it focuses on the future recipe recommendations.
It is implemented in 2 parts. The first one is done
using the ingredients as the primary tool for the
recommendations and the second one focusing on the
user’s previous behaviour. The reason behind trying
two different methodologies is to try and analyse the
concept of filter bubbles and how to burst them. The
goal behind every recommendation is to be of a user’s
taste but also try to stray of a straightforward personal
Additionally, it is expected that the following two
approaches give completely different results, so the
idea is to create a new, hybrid approach that would
take into account the habits of the user, but would also
add a new perspective and different
recommendations, thus bursting the filter bubble that
could happen.
4.2.1 Text Similarity Recommendations
The purpose of generating recommendations based
on text similarity leads to recipes that are ingredient-
wise to the ones that the user already interacted with.
The algorithm for generating recipe embeddings that
was taken into consideration was BERT (Devlin et
al., 2018). BERT stands for Bidirectional Encoder
Representations from Transformers. BERT learns
contextual relationships between words in a sentence
or text.
The goal here was to get recommendations based
on the recipe’s name - which almost always includes
the ingredients of the recipe. A sentence-transformer
BERT was chosen to get the embeddings for the
recipes with help of the raw recipes data-set. The raw
recipes data-set does not contain pre-processed data,
therefore all of the attributes for one recipe are textual
and not numeric, which is good for BERT to take
them as text and encode them by its rules. The text
similarity embeddings actually rely on the recipe’s
name, which in this case is highly productive because
of the way the recipes are named (e.g. arriba baked
winter squash Mexican style). As said, the recipe’s
name includes some of the ingredients (mostly the
key ingredients), the cooking technique (e.g. baked)
and other adjectives which additionally describe the
food recipe (e.g. Mexican food).
Large numbers of the recipes included in the data-
set had zero reviews and ratings - never seen recipes,
so they are not taken into consideration while
building the recommendation system based on text
similarity for better accuracy. Also, it is relevant to
mention that some of the recipes had rather strange,
creative (e.g. smells like Sunday chicken fricassee
with meatballs) names that may confuse the text
Multimodal Analysis of User-recipes Interactions
similarity model in the process of generating the
The newly generated recipes embeddings by text
similarity are then used for cosine similarity
calculation. In other words, there is a pairwise
comparison or cosine similarity between pairs of
recipes - each recipe compares with the rest of the
recipes. This computation is done on just one part of
the embeddings because of the big computation
power that is needed. Cosine similarity,
mathematically, measures the cosine of the angle
between two vectors in a multi-dimensional space, in
this context two recipes transformed into
embeddings. When plotted on a multi-dimensional
space, where each dimension corresponds to a word
in the recipe, the cosine similarity captures the
orientation (the angle) of the recipes. The cosine
similarity is advantageous because even if the two
similar recipes are far apart by the Euclidean distance
they could still have a smaller angle between them. If
the angle is smaller, the similarity will be higher and
After the cosine similarity is calculated, the
closest or the most similar recipes to a given one can
be found - based on the highest cosine similarity
The recommendations from the text similarity
model, as expected, were not surprising. BERT using
text similarity shows a great focus on the key
ingredients of the recipe. If the recipe of interest was
a dessert for example, all of the recommendations
would also be desserts. Or if a recipe has chicken as
its key ingredient, then all of the recommendations
would also contain chicken, nevertheless if it is a
breakfast or lunch. It can be concluded that text
similarity would be a great deal if the goal is to
generate recommendations only based on an
ingredient of preference, but only to the point where
no other factors are taken into consideration.
4.2.2 Node Embedding Recommendations
The idea of generating recommendations based on the
whole graph is to find recipes that the user might like
based on the whole graph structure.
The algorithm that was chosen for generating
recipe embeddings in the process of creating
recommendations was Metapath2Vec along with
cosine similarity calculations.
As already described, the bipartite graph has a
simple structure, consisting of two types of nodes
(recipes and users) connected with a link that
represents an interaction. There are no connections
between two users or two recipes.
Given this structure, with the help of
Metapath2Vec and Word2Vec node embeddings
were generated. The Metapath2Vec model formalizes
meta-path based random walks to construct the
heterogeneous neighbourhood of a node and then
leverages a heterogeneous skip-gram model to
perform node embedding (Dong et al., 2017). The
Metapath2Vec algorithm includes 2 steps: First, Use
uniform random walks to generate sentences from a
graph. A sentence is a list of node IDs. The set of all
sentences makes a corpus. The random walk is
driven by a metapath that defines the node type order
by which the random walker explores the graph.
The random walks have fixed maximum length
and are controlled by the list of metapath schemas.
Second, the corpus is used to learn embedding vector
for each node in the graph. Each node ID is
considered a unique word/token in a dictionary that
has a size equal to the number of nodes in the graph.
The Word2Vec algorithm is used for calculating the
embedding vectors.
In this case, the metapath which defines the node
type order of the random walk consists of two
schemes which change the order of the nodes. To be
more precise, each metapath schema must start and
end with the same type of node (e.g. start and end with
a recipe or a user). In this particular case, there is a
scheme for a user to a recipe, ending with a user, and
from a recipe to a user, ending with a recipe. Schemes
that included a walk between the same type of nodes
(e.g., from a user to a user) weren’t taken into
consideration because of the graph structure which
states that there are no interactions between the same
type of nodes.
Given the newly constructed node embeddings,
cosine similarity was again used as a measure of
nodes similarity. Given a particular recipe node, the
top 10 similar nodes were considered to be the most
similar ones to that node. So in order to test the
previous hypothesis, explore more about the
difference between the two algorithms and to find the
possible link that connects them both, few
experiments were run.
The results from Metapath2Vec came back rather
different than the ones with the previous algorithm. It
was concluded that the most similar recipes to a
particular recipe, were more diverse and varied within
multiple types of meals (dessert, breakfast, lunch,
dinner), opposite to BERT that sticks to a same type
of meal, and almost same ingredients, making the
recommendation very similar to the past food choices
of the user.
HEALTHINF 2022 - 15th International Conference on Health Informatics
Text similarity recipe recommendations for a lunch
come back as lunches with focus on the ingredients of
the recipe, whereas the results from the
Metapath2Vec vary among different types of meals
such as lunch, dessert, or dinner with no focus on the
ingredients whatsoever.
To test the previous assumptions, a few
experiments were conducted to compare the
recommendations from text similarity and node
embeddings. For this purpose, the following steps are
applied: Choose a random user, choose a recipe that
the user has interacted with, make text similarity
based recommendations for that recipe, make node
embedding recommendations for that recipe and last
check if text similarity recommendations and node
embedding recommendations overlap.
So, 100 random users were taken and for each of
them, a random recipe from the list of their
interactions was chosen. The next step was a
generation of 10 text similar recommendations and 10
node-embedding recommendations. The
recommendations were compared with each other,
and they didn't overlap in any of the cases, which
means that there is a difference between them.
Text similarity focuses on the words while node
embeddings are calculated based on the whole graph
structure, aka the user's previous behaviour.
However, this does not mean that we can overthrow
any of these approaches, but moreover gives an
opportunity to create a new, hybrid one that combines
the previous two. It is expected that by combining an
approach that focuses on a user’s past behaviour and
an approach that focuses on the ingredients, the filter
bubble that personal recommendations can create, can
easily be burst and a new perspective can be shown.
The overall goal of the whole research is to test
various approaches for making recipe
recommendations, and combine them into one hybrid
approach that takes the best of them.
To do so, we analyse user-recipe interactions from
two aspects, through future recipe recommendations
and through temporal interaction predictions.
In the first aspect after analysing the results from
the recipe recommendations, two conclusions were
derived. The recommendations based on node
embeddings are more diverse. For example, if you are
looking for a recommendation for a starter meal, the
recommender will offer a main course or a dessert
rather than a similar starter meal, whereas text
similarity as different approach focuses on the
ingredients and will offer similar starter meals or
meals that have similar ingredients. The text
similarity method would give great recommendations
for people who like to stay consistent with their food
choices and like to repeat the same food pattern and
ingredients. The approach with node embeddings
would give great recommendations for people who
like to try out new things and follow the food trends.
Furthermore, knowing that filter bubbles are not the
most desirable situation, the combination of these two
approaches would prevent their formation (Aridor et
al., 2020).
This work was supported by the Slovenian Research
Agency (research core funding programmes P2-
0098); the European Union's Horizon 2020 research
and innovation programme under grant agreement
863059 (FNS-Cloud, Food Nutrition Security) and
under grant agreement 101005259 (COMFOCUS).
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