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Nudging Towards Health in a Conversational Food
Recommender System Using Multi-Modal Interactions and
Nutrition Labels
Giovanni Castiglia
1
,Ayoub El Majjodi
2
,Federica Calò
1
,Yashar Deldjoo
1
,Fedelucio Narducci
1
,
Alain Starke2,3 and Christoph Trattner2
1Polytechnic University of Bari, Bari, Italy
2Department of information science and media studies, University of Bergen, Bergen, Norway
3Marketing and Consumer Behaviour Group, Wageningen University & Research, Wageningen, The Netherlands
Abstract
Humans engage with other humans and their surroundings through various modalities, most notably speech, sight, and
touch. In a conversation, all these inputs provide an overview of how another person is feeling. When translating these
modalities to a digital context, most of them are unfortunately lost. The majority of existing conversational recommender
systems (CRSs) rely solely on natural language or basic click-based interactions.
This work is one of the rst studies to examine the inuence of multi-modal interactions in a conversational food
recommender system. In particular, we examined the eect of three distinct interaction modalities: pure textual, multi-modal
(text plus visuals), and multi-modal supplemented with nutritional labeling. We conducted a user study (
𝑁
=195) to evaluate
the three interaction modalities in terms of how eectively they supported users in selecting healthier foods. Structural
equation modelling revealed that users engaged more extensively with the multi-modal system that was annotated with
labels, compared to the system with a single modality, and in turn evaluated it as more eective.
Keywords
Personalization, Health, Food recommendation, Digital Nudges, Nutrition labels
1. Introduction and Context
Conversational recommender systems (CRSs) represent
a hotly debated area of study in the eld of information
seeking [
1
,
2
]. They combine the power of recommen-
dation algorithms with conversational strategies. Using
multi-turn conversations, CRSs are able to collect users’
nuanced and dynamic preferences in more depth, which
can enhance recommendation outcomes and user experi-
ence. CRSs are utilized in a variety of domains, including
medical diagnosis [
3
], e-commerce [
4
], and entertain-
ment [
5
,
6
]. Only a few studies have investigated their
merit for food recommendation [
7
], and in particular for
encouraging users to make healthier food decisions.
Over 60% of all deaths are caused by non-
communicable diseases, which are preventable by
tackling risk factors, such as attaining a healthy food
RecSys’22: 4th Workshop of Knowledge-aware and Conversational
Recommender Systems, Seattle, WA, USA
g.castiglia@studenti.poliba.it (G. Castiglia);
ayoub.majjodiu@uib.no (A. E. Majjodi); f.calo8@studenti.poliba.it
(F. Calò); yashar.deldjoo@poliba.it (Y. Deldjoo);
fedelucio.narducci@poliba.it (F. Narducci); alain.starke@uib.no
(A. Starke); christoph.trattner@uib.no (C. Trattner)
https://www.christophtrattner.info/ (C. Trattner)
0000-0002-7478-5811 (A. E. Majjodi); 0000-0002-6767-358X
(Y. Deldjoo); 0000-0002-9255-3256 (F. Narducci);
0000-0002-9873-8016 (A. Starke); 0000-0002-1193-0508 (C. Trattner)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
intake [
8
]. While our food decisions are driven by
our overall preferences, the food selection process is
extremely contextual and inuenced by a variety of
factors, such as the user’s mood and dietary constraints.
Moreover, many of the decisions are made spontaneously
and consumers’ judgments are inuenced by factors
unrelated to the food content, such as their perception
of the food’s visual characteristics [
9
]. For instance,
the packaging of items with nutritional labels can
serve to highlight the nutritious nature of the food
(cf. [
10
]). Moreover, people generally prefer food that
has a more visually appealing presentation, such as
food that is presented in an attractive way [
11
]. People
are willing to pay extra for food whose ingredients
are tastefully/attractively organized, and restaurants
strive to generate Instagram-friendly photographs by
enhancing the color composition of their plates.
To surface eective and healthy food recommenda-
tions it is crucial to understand these underlying decision
factors. Regrettably, the large majority of existing con-
versational recommender systems [
12
,
13
] only consider
a single type of interaction, such as natural language or
click-based interaction, thereby neglecting a wealth of
information in the actual imaging of meals [
14
]. The goal
of the present work at hand is to employ a new conver-
sational model for food recommendation that permits
more natural, multi-modal user-system interaction.
To attain this goal, this paper introduces a multi-
modal conversational food recommender system (MM-
CFRS). It implements dierent user-system interaction
modes, along with nutrition labelling in order to assist the
user in making dietary decisions. Our objective is to ex-
amine the eects of three distinct interaction modes: pure
textual, multi-modal (text plus visuals), and multi-modal
supplemented with nutritional labeling. While multi-
modal conversational information seeking (MMCIS) is
gaining attention by the research in the RecSys/IR/HCI
communities [
15
,
1
,
16
], only a few practical studies have
been published that focus on topics other than food and
health, such as conservational systems on tourism [
17
]
and fashion [
18
,
19
]. In the eld of food recommendation,
Elsweiler et al.
[20]
provide a good frame of reference
for recent advances in the eld of food recommender
systems in general. Specically for conversational sys-
tems, Barko-Sherif et al. [
21
] investigate the possibility
for conversational preference elicitation in a food recom-
mender environment, using a Wizard of Oz study design
(see also [
22
]). Using a between-groups approach, they
compare spoken and text-input chat interfaces and re-
ported that such interfaces are useful for users to describe
their needs and preferences. In other studies, Samagaio
et al. [
23
] present a RASA-based chatbot that can rec-
ognize and categorize user intentions in the conversa-
tion aimed to elicit food preferences for recommendation
purposes. Another study of Samagaio et al. [
24
] ap-
plies more knowledge-based elements based on word em-
bedding to optimize conversational ingredient retrieval.
These studies, however, focus less on aspects pertaining
to health, health labelling, or elicitation modalities. In
a non-conversational recommender context, El Majjodi
et al. [
25
] recently indicated that nutritional labels can
reduce user’s choice diculty in the context of conven-
tional non-conversational recommendation system. The
primary distinction between our work and previous stud-
ies is the lack of multiple modalities (typically only text
is used), as well as that only a few studies (e.g., [
25
]) have
used nutrition labelling.
To summarize, the goal of this study is to compare the
impact of three user-system interaction and explanation
modalities (textual, multi-modal, and multi-modal with
nutritional labels) on both behavioral aspects (what type
of recipe is chosen? How healthy is that recipe?) and
evaluation aspects (how does the user evaluate the sys-
tem or their chosen recipe?). Using a mediation analysis
(structural equation modelling), we answer the following
research question:
•
RQ: To what extent do dierent interaction modal-
ities aect a user’s recipe choices and evaluation
in a conversational food recommendation sce-
nario?
To address this question, we consider dierent dimen-
sions of analysis. This includes system interaction length,
presentation time, healthiness of recipes chosen and a
user’s level of choice satisfaction and experienced system
eectiveness.
2. System Design
In this section we describe the features of our conversa-
tional food recommender system, which supports users
in making healthier choices.1
We designed a system-driven conversation in which the
system requires user feedback (response/input) to con-
tinue. The main steps of the conversational ow are
shown in Figure 1. Users can interact with the system us-
ing both buttons and textual messages
2
. The main steps
of the interaction are reported below:
•
Food category acquisition: The user was presented
with a choice of four dierent food categories
that were considered in this work: Pasta, Salad,
Dessert, and Snack.
•
User constraints acquisition: The user was then
prompted to indicate any potential dietary con-
straints. Initially, the system used an interface
with a single checkbox for each of the most preva-
lent intolerances and allergies: Lactose, Meat, Al-
cohol, Seafood, Reux, Cholesterol, Diabetes. Af-
terwards, the system asked the user to disclose a
list of ingredients she could not consume.
•
Preference elicitation: According to the con-
straints specied by the user, the user was
prompted to submit preferences for ve of the
dishes proposed by the system. Each dish was
accompanied with two buttons: “Like” and “Skip”.
The skip option was provided to encourage users
to inspect an addition dish, which was retrieved
from the randomly sorted menu. The retrieval
was based on a random active learning strategy.
This way, users were encouraged to like ve
dishes they were interested in, aer which the
user prole was built by the system.
•
Processing: The system constructed the user pro-
le by analyzing the user’s ve preferences from
the previous stage. The cosine similarity was
computed between the user prole and each of
the available foods in the catalog, to provide a list
of dishes from which recommendations would
be selected. The algorithm also provided a list
of dishes ranked according to their healthiness
(based on their FSA score; see Section 3).
1
Code and recipe data used for the implementing the chatbot will
be released in a GitHub repository aer peer-review and linked in
the paper.
2An anonymized video demo of the three versions of our system is
available online at https://tinyurl.com/mtzxr2sw
Figure 1: Our conversational recommender system flow.
For each food category we built a matrix con-
taining the TF-IDF representation (dish vs. ingre-
dient) of dishes in the catalog. The higher the
TF-IDF score, the greater the ingredient’s signi-
cance to this dish (as opposed to other dishes).
•
Recommendation and explanation: The system
provided two personalized recommendations,
based on the user’s preferences. The system con-
strained the retrieval to ensure that the two op-
tions diered in terms of healthiness, so that one
option was healthier than the other. Thus, the
algorithm provided a description of the suggested
dishes. Specically, it explained why the second
dish was healthier than the rst and why the ad-
vice was made. The user would then be prompted
to select one or request a new recommendation.
The two recommended dishes were chosen using
the following strategy: The rst dish would be
the most similar to the user prole, while the sec-
ond dish (the healthier alternative) was selected
from a list of most similar dishes ranked on their
FSA scores, selecting the healthiest one (i.e. with
the lowest FSA score).
Three dierent interaction modes were implemented
by modifying the values associated with the two manipu-
lated variables: interaction
𝐼
and explanation
𝐸
, according
to Table 1.
In the Pure text version (T + T), the system communi-
cates with the user solely through text, displaying sim-
ply the dish titles and oering textual explanations of
the food recommendations. In the Multi-modal version
(MM + T), the system engages the user in a multi-modal
Table 1
Dierences between three implementations of the system.
Interaction Mode I E
Pure text (T) T T
Multi-modal (MM) MM T
Multi-modal with labels (MM-Label) MM MM
manner by displaying the name and image of each dish
throughout the dialogue. However, the supplied explana-
tion remains textual. For the rst dish, the explanation
can be like ”I recommend these dish because I know
that you have diet constraints due to: meat, zucchini.
The rst dish I proposed contains ingredients that you
might like: carrot, lemon, tuna, olive oil”. For the sec-
ond recommendation, the explanation further provides
information about macro nutrients quantities of the two
recommended dishes and can be in the form of ”The
second dish I proposed has less calories (54 Kcal) than
the rst one (123 Kcal) and has less fats than the rst
one. The third version MM-Label (MM + MM) likewise
employs a multi-modal interaction approach, but it also
makes use of nutritional explanations in the form of a
front-of-package nutrition label with FSA’s Multiple Traf-
c Lights (MTL) [
25
]. MTL nutrition labels depicted the
intake adequacy of a dish in terms of energy and nutri-
tional content, along ve dimensions: energy (kcal), fat,
saturates, sugars, and salt. This adequacy, per serving
and per 100g, was depicted using the colors green, yellow
and red, where green indicated a dish to adhere to the
nutritional intake guideline, while red indicated that the
content was unacceptable. These labels were generated
Figure 2:
The three implementations of the system. Some details displayed on the interface, such as the chatbot’s and authors’
names are anonymized and will be added aer peer review.
for each dish by following the directives of Food Standard
Agency and UK department of health [26].
Figure 2depicts a snapshot of the chatbot prototype,
visualizing the dierent interaction phases.
In the Textual (T) version, the user received recom-
mendations identied by only the names of the dishes
(e.g., Cupcake Princess’ Vanilla Cupcakes, Floating Island
II). The recommendations were followed by textual ex-
planations, based on the ingredients in the dish that the
user likes. A comparative analysis of the nutritional facts
(e.g., ‘less sugars’) would also be provided. In the Multi-
modal (MM) version, the system additionally provided
images of the recommended dishes. The explanation was
similar to the one presented in the Tversion. Finally,
the Multi-modal with labels (MM-Label) version provided
nutritional labels that were annotated to the depicted
images (e.g., Sugar 2.3g, Fat 10.7g, etc.) presented with
red, yellow, and/or green colors according to the FSA
score. As stated previously, following the presentation of
the recommendations, we provide the user with an expla-
nation that helps her comprehend the health benets of
the second alternative above the rst, which is the dish
that best matches her preferences. This is accomplished
either by text (T and MM variants) or a multiple trac
light nutritional label (MM-Label).
The user can accept one of the two dishes proposed or
can ask for another recommendation.
3. Experimental Evaluation
To evaluate the extent to which dierent versions of the
chatbot aected users’ evaluations and decisions, we re-
cruited 195 participants from Amazon MTurk to use our
system. Participants had to have a hit rate of 95% at least
and were compensated with 2 dollars. On average, user
required around 15 minutes to complete the study.
3
Users
3
The research conformed to the ethical standards of the Norwegian
Centre for Research Data (NSD). The collected data will also released
Table 2
Questionnaire items used in the confirmatory factor analysis. Alpha denotes Cronbach’s Alpha, AVE denotes the Average
Variance Explained, indicating construct validity if AVE > 0.5. Items in gray and without loading were omitted from analysis.
Choice Satisfaction did not form a sensible aspect, because of a lack of construct validity.
Aspect Item Loading
I think, I would enjoy eating the dish I have chosen in the end
Choice Satisfaction I would recommend the dish I’ve chosen in the end to others
My chosen dish could become my favorite
It was easy to make my final choice on the dish 0.737
I interacted a lot with the system before getting the dish of my choice
System Eectiveness The explanation influenced my final choice of dish
I think, that I would use this system frequently
Alpha = 0.740 I found the system easy to use and understand 0.724
AVE = 0.534 I felt very confident using the system 0.661
I would imagine that most people would learn to use this system very quickly 0.722
performed the processes outlined in Section 2, interact-
ing with our chatbot for preference elicitation, evaluating
recipe recommendations, selecting one recipe, and evalu-
ating the experience. A user’s experience was evaluated
through choice satisfaction and system eectiveness, us-
ing questionnaire items that were evaluated on 5-point
Likert scales.
Chosen recipes were evaluated according to their
healthiness. This was evaluated using the FSA score [
27
].
Each recipe was scored between 4 and 12, where 4 indi-
cated that all four nutrients (sugar, fat, saturated fat, salt)
adhered to nutritional guidelines per 100g [
9
,
28
], while
12 would indicate that a recipe was unhealthy because
of all nutritional contents being too high.
The responses to the evaluation questionnaire item
were submitted to a conrmatory factor analysis (CFA;
see Table 2). Unfortunately, we could not infer a reli-
able construct for choice satisfaction, as the variance
explained by the questionnaire items was too low, while
Cronbach’s Alpha was only acceptable (0.60). Other items
were dropped from the system eectiveness aspect be-
cause of low factor loadings.
We organized the dierent factors (e.g., conversation
time, condition factors) and aspects (i.e., system eective-
ness) into a path model using Structural Equation Mod-
elling. Figure 3depicts the resulting model, which had de-
cent t statistics:
𝜒2(17) = 28.064
,
𝑝 < 0.05
,
𝐶𝐹 𝐼 = 0.969
,
𝑇 𝐿𝐼 = 0.954
,
𝑅𝑀𝑆𝐸𝐴 = 0.058
,
90% − 𝐶𝐼
:
[0.009, 0.095]
.
The relevant AVEs of the aspects was suciently high to
form a path model [29].
Our analysis revealed that the MM-Label condition
with nutrition labels (MM-label) stood out in terms of
how long users interacted with our chatbot. Figure 3
illustrates this, while the use of multi-modal approaches
alone had no eect on the interaction or evaluation fac-
tors considered. For MM-Label, our mediation analysis
suggested that in the MM-Label condition, the conversa-
online in the project’s GitHub repository aer peer-review.
tion duration was signicantly longer (
𝑝 < 0.05
) than in
the text-based condition . This indicated that the usage
of nutrition labels aected conversation time, on top of
the other modalities.
The duration of the conservation aected, in turn, the
evaluation of the user. Inferred from our conrmatory
factor analysis (cf. Table 2), users who interacted with
the chatbot for longer periods of time indicated greater
levels of system eectiveness (
𝑝 < 0.01
). This indicated
that an extended engagement did not frustrate users. In-
stead, it indicated that they were enthusiastic about using
the system. Figure 3also shows that the healthiness of
chosen recipes was not signicantly related to any of the
other aspects or factors. Note that the MM-Label condi-
tion led the healthiest recipe choices, but the dierences
with the other conditions were not signicant.
4. Conclusion and Future Work
We have presented a novel chatbot-like recommender
system that introduces multi-modality in interaction with
user, presentation of results and explanation of the rec-
ommendations with nutrition labels in a conversational
scenario. We have designed and analyzed the impact
of three distinct version of our chatbot: pure textual,
multi-modal (use of text and images), and multi-modal
supplemented with nutritional labels.
Our experimental evaluation reveals that our chatbot
is the most eective when accompanied by explanatory
labels. This is indicated by the length of conversation, as
well as by the user’s evaluation of the system eective-
ness.
Limitations to this study could be viewed from dif-
ferent viewpoints. In terms of analysis, we have been
unable to infer the choice satisfaction evaluation aspect.
Other research have demonstrated that decision satisfac-
tion is a good predictor of post-interaction engagement
with selected item, such as for household energy con-
Figure 3:
Structural Equation Model (SEM). Numbers on the arrows represent the
𝛽
-coeicients, standard errors are denoted
between brackets. Eects between the subjective constructs are standardized and can be considered as correlations, other
eects show regression coeicients. Aspects are grouped by color: Objective system aspects are purple, behavioral indicators
are blue (note: the FSA score represents recipe unhealthiness) and experience aspects are orange. The thinner arrows are
non-significant relations, in addition: ∗∗∗ 𝑝 < 0.001,∗∗ 𝑝 < 0.01,∗𝑝 < 0.05.
servation [
30
]. Moreover, rather than relying solely on
system-driven interaction, it might be intriguing and nat-
ural to investigate user-driven scenarios in which users
might query the system with an image and textual query.
The food categories considered in this work (pasta, salad,
dessert, snack) could additionally be expanded to include
more meal categories and their combinations, such as
to create a complete meat (rst dish, second dish and
vegetables). On top of that, the distinctions between var-
ious label modalities are an additional intriguing topic
we wish to investigate more in-depth [31].
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