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Joint Proceedings of the ACM IUI 2021 Workshops, April 13–17, 2021, College
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# Changing Salty Food Preferences with Visual and Textual Explanations in a
Search Interface

Arngeir Berge<EMAIL_ADDRESS>NORCE Norwegian Research Centre,
P.O.Box 22 Nygårdstangen, 5838 Bergen, Norway  Vegard Velle Sjoen  University
of Bergen, P.O.Box 7800, 5020 Bergen, Norway<EMAIL_ADDRESS>Alain Starke
<EMAIL_ADDRESS>Wageningen University & Research, 6708 PB Wageningen, The
Netherlands  Christoph Trattner<EMAIL_ADDRESS>
(2021)

###### Abstract

Salt is consumed at too high levels in the general population, causing high
blood pressure and related health problems. In this paper, we present results
of ongoing research that tries to reduce salt intake via technology and in
particular from an interface perspective. In detail, this paper features
results of a study that examines the extent to which visual and textual
explanations in a search interface can change salty food preferences. An
online user study with 200 participants demonstrates that this is possible in
food search results by accompanying recipes with a visual taste map that
includes salt-replacer herbs and spices in the calculation of salty taste.

###### keywords:

Food Preferences Salt Sodium Replacement Search Interface Explanations

##  1 Introduction

Worldwide obesity levels are increasing [1]. A main issue is the high
prevalence of unhealthy foods available, both offline and online [2, 3]. On
the other hand, technology has been shown to be useful to tackle obesity. Food
recommender technology, for example, has demonstrated the potential to change
people’s eating behavior [1, 4]. Yet, less explored is the principle of
“search”, the main means of finding information about food on the Web.

To contribute to this little researched area, we have focused on the extent to
which search interfaces can change people’s eating preferences. One recent
study shows healthy recipes can be boosted by presenting attractive food
images alongside them [5], overcoming possible innate preferences for
unhealthy food [3].

In this work, we focus on salty food preferences and how to change these
during the search process. The main principle is to replace food that has a
high salt content with less salty food. The food items under investigation are
online food recipes. To support possible changes in user preferences, we
emphasize the preference of salt replacers in food through visual and textual
explanations. Most people who like salty taste in food are equally satisfied
if salt is reduced slightly, as long as substitute herbs or spices are added
[6]. Such salt replacers (SRs) are ingredients like oregano and garlic, which
mimic the taste sensation triggered by consuming salt. To promote salt-
replacer recipes (SR recipes) we test an approach in which the visual and
textual explanations of food are altered in a search interface. The intention
of our explanations is to boost SR recipes by educating the user about the
healthier nutritional content. Such an intervention is referred to as a
“Boost”, a type of nudging that promotes small changes in behavior through
education [7].

Main objective: We seek to show that visual and textual explanations of salt
content and salt replacers (SRs) in a search interface can alter salty food
preferences. Focusing on the online recipe domain, we posit the following
research questions:

  * •

RQ1: To what extent do salty food preferences change due to visual and textual
explanations on a recipe’s salty taste?

  * •

RQ2: To what extent do other recipe and user characteristics affect salty food
preferences?

In the remainder of this paper, we first discuss relevant work related to this
research (cf. Section 2), positioning our approach. Then, we describe the
contents of our user study, including recipe dataset and our visual and
textual explanations for salty taste. Finally, we describe our main results
and discuss their implications.

##  2 Background

In this section, we first introduce key health implications of consuming food
high in salt content. Secondly, we present studies that have examined how food
preferences can be shifted towards healthier options (i.e. digital nudging),
by focusing on how food is presented rather than changing what is presented.
Thirdly, we show how this work is based on earlier research in the field, and
where it makes a contribution.

###  2.1 Salt in Food and Salt Replacers

High salt/sodium content in food is one of the fundamental indirect causes of
disease and death [8, 9]. Cardiovascular diseases can be attributed to
increased blood pressure, which is found to be responsible for 62% of strokes
and 49% of coronary hearth diseases. Starting a downward spiral, a person’s
blood pressure is increased by high salt intake [8], which can often be
attributed to high levels of consumption of preprocessed food [1].

The high prevalence of salt in food is also notable online. Trattner et al.
[1] show that most recipes on the world’s largest recipe website,
Allrecipes.com, are relatively unhealthy. To make matters worse, in terms of
health, most personalization algorithms tend to prioritize popular, yet
unhealthy items in their search results, which tend to surpass WHO and FSA
guidelines for salt intake.

The healthiness of online recipes can be improved by swapping salt content for
replacement ingredients that mimic the taste. Salt-replacer (SR) herbs and
spices can do this and have health benefits in their own right, sometimes even
lowering blood pressure [10]. Examples of such ingredients are garlic, oregano
and rosemary, which can be found in various online recipes [1]. However,
personalization algorithms in search have yet to prioritize recipes that
contain SR ingredients (SR recipes) so that users can reap their health
benefits.

###  2.2 Digital Food Nudges

Besides changing what recipes are recommended, one way to promote SR recipes
is to highlight them in a choice interface. To change the choice context
without altering what is presented can be referred to as (digital) nudging.
Cadario et al. [11] classify three types of nudges: cognitively-oriented (e.g.
educating the user about nutritional values), affectively-oriented (e.g.
affecting a user’s feelings by putting happy faces on healthy products), and
behaviorally-oriented nudges (e.g. changing how options are presented in a
supermarket shelf). The latter is found to be the most effective offline. This
suggests that healthy alternatives can be promoted in a digital interface,
such as a search user interface (UI), by re-ranking recipes based on health.
In addition, accompanying recipes with a cognitively-oriented explanation
about which ingredients can replace salt content might also “nudge” users
towards healthy options, as the health benefits of SR ingredients are
relatively unknown [12]. This might affect healthy choices in the long term.

A specific type of nudging is called “Boosting”. “Boosts” promote small
changes in behavior, by educating users about the suggested changes at the
same time [7]. We believe that it is possible to both make it easier for users
to select healthy recipes and increase their knowledge, by calculating a
wholesome salty taste score based on research about SRs, as well as educating
users about how selecting SRs can accomplish a salty taste with less sodium.

###  2.3 Differences to Previous Research

The key differences between this research and the works mentioned above are
the focus on the type of interface, the food preferences, and the type of
nudges being used to change food preferences.

With regard to user interface design, a lot of research is devoted to
recommender interfaces [3], while our research is on food search. Another key
difference is the type of preferences we consider. While previous research
examined food preferences in general [2], we focus on salty food preferences,
as high salt intake is shown to be the main cause for current cardiovascular
diseases [8]. Finally, compared to previous work, this study is among the
first to investigate the use of taste maps and textual explanations to change
people’s salty food preferences.

##  3 Methods

This section presents the methods used and applied in our research. In
particular, we discuss our recipe dataset sample, reveal details about the
visual and textual explanations and how we developed these and, finally,
describe the design of our online study and how this study was carried out.

###  3.1 Recipe Dataset

To address our research questions, we selected main courses from a recipe
database, obtained from the recipe website Allrecipes.com and provided by [1].
The dataset comprised a sub-sample of 1031 recipes with detailed information
about ingredients, directions and nutritional values. To perform our research
we selected two times six recipes corresponding to two search result sets in
our prototype, see Table 1. Six of the recipes answered a search for “chicken”
and the other six answered a search for “pork”. Each of the search result sets
had half-half recipes with and without SR ingredients. When selecting SR
recipes we looked for SRs that appear in the literature [6, 13, 14, 10] and
ended up with recipes containing one or more of the SR ingredients garlic,
rosemary or oregano. The search result sets were composed to reflect the
variety found in real-world food sites by covering a wide spectrum of sodium
content measured per serving. Each search result set also mirrored the large
dataset in that some recipes contained SR ingredients and some did not. In our
case, three SR recipes and three Non-SR recipes.

Table 1 shows the recipes for the two search result sets used in the online
user study. For SR recipes we calculated an SR-boosting score. For Non-SR
recipes we let the SR-boosting score be similar to the sodium score.

Table 1: The selected recipes in the chicken and pork search result sets with sodium score, salt-replacer-boosting score and salt-replacer ingredients. Bold numbers show where the sodium score has been boosted. Note: We have marked recipes without salt replacers like for example garlic with N/A. Recipe sets | Recipe title | Sodium score | SR-boosting score | Salt replacers
---|---|---|---|---
Chicken | Slow Cooker Sweet and Tangy Chicken | 0.81 | 0.97 | Garlic
Tomato Chicken Parmesan | 0.69 | 0.69 | N/A
Chicken Scarpariello | 0.61 | 0.73 | Rosemary, garlic
Oh-So-Good Chicken | 0.4 | 0.4 | N/A
Chicken Marsala | 0.3 | 0.36 | Oregano
Baked Lemon Chicken with Mushroom Sauce | 0.11 | 0.11 | N/A
Pork | North Carolina-Style Pulled Pork | 0.94 | 0.94 | N/A
Pork Chops for the Slow Cooker | 0.74 | 0.89 | Garlic
Skillet Pork Chops with Potatoes and Onion | 0.6 | 0.6 | N/A
Slow Cooker Teriyaki Pork Tenderloin | 0.41 | 0.49 | Garlic
Roast Pork with Maple and Mustard Glaze | 0.29 | 0.29 | N/A
Pork Marsala | 0.2 | 0.24 | Oregano, garlic

###  3.2 Designing Salty Taste Explanations

We developed a visual explanation of taste that we call a taste map, see
Figure 1. The taste map was inspired by other visual taste descriptions in the
literature [15]. Our visual taste explanation is a pentagram with one axis for
each of the five basic tastes. For this study, we only calculated values for
the salty taste axis. Had values for all axes been calculated, a shape would
emerge to show a complete taste profile that would enable users to look for a
variety of visual shapes when browsing search results or recipe presentations.

Figure 1: Example of a taste map presented for each recipe. Only the salty
taste axis is calculated in this study and therefore the coloration fades
towards the other axes.

The taste map covered a scale from 0 to 1 on the salty axis, with a
granularity of 0.1. We anticipated that many of our participants would expect
a recipe with a mean sodium level to be displayed with 0.5, the middle value
of our scale. The sodium content per serving in the larger dataset was
normally distributed. The mean was 0.9 grams and the 95 percentile was 2.0
grams. We therefore made it simple by regarding the maximum sodium level as
the mean times two, which is 1.8 grams. Any serving with 1.8 grams sodium or
more would turn up as 1 on our scale. A mean sodium level would turn up as 0.5
on our scale. In one of the experiment conditions, the value of the taste map
was boosted when shown alongside SR recipes. The taste map functioned as a
visual salty taste explanation for all recipes at all times in our experiment.
In the SR-boosting condition, however, the taste map had a heightened
explanatory role by showing visually that SRs contribute to the perceived
salty taste. Calculating the contribution of salt replacers to salty taste is
a subject of food chemistry and beyond the scope of this paper. To be able to
perform our experiment, we took a pragmatic approach and added 20% to the
sodium score to calculate an SR-boosting score for SR recipes.

In addition to the visual explanation, we designed a textual explanation in
our search prototype to boost SR recipes, as depicted on the bottom right of
Figure 2. The textual explanation had two parts: First, the headline “Healthy
ingredients enhancing salty taste”, followed by an explanation like “Oregano
and garlic joins the salt in enriching the flavors of this recipe.”

###  3.3 Online User Study

We used our recipe dataset and salty taste explanations in our online user
study.

####  3.3.1 Participants

To have a diverse pool of participants, we used two crowdsourcing platforms.
We recruited a total of 200 participants: 100 on Prolific, 100 on Amazon
MTurk. We only sampled U.S. nationals, as ingredient amounts were denoted in
U.S. metrics. As is common in research, we had a different required approval
rate for the two platforms [16], 90% and 98% respectively. Prolific
participants were reimbursed with 2 GBP for participation as we assumed that
our study took 14 minutes, while “MTurkers” were compensated with 0.5 USD when
we found out that the mean completion time was much shorter (7 minutes). The
eventual sample comprised 54.8% males, with a mean age of 37.6 years
($SD=14.16$).

####  3.3.2 Prototype

The prototype had web pages for consent and questions before and after the
experiment. More notably, it had web pages for search samples (see upper part
of Figure 2). A search bar prefilled with a search term was shown together
with six search results. To the right of each recipe title was a miniature
taste map, so that users could quickly scan the taste of each recipe in the
results. The prototype also had separate web pages to present the full recipes
that appeared in the search results (see lower part of Figure 2). Each page
contained the recipe title, an image of the meal, ingredients, cooking
directions and nutritional info, in addition to a taste map section. Figure 2
also shows side-by-side the two ways SR recipes were presented. On the left-
hand side the salty taste value in the taste map was based on sodium score. On
the right-hand side, the salty taste value in the taste map was boosted
visually and accompanied by a textual explanation.

Figure 2: Top: One of the two search result sets. Bottom left and right: A
single participant would see this recipe in only one of the left/right
presentation states. Since the recipe had salt-replacer ingredients, it could
be presented in two states: On the left with a taste map based on sodium score
and on the right with a textual explanation and a taste map based on SR-
boosting score. Non-SR recipes could only have the state shown on the left-
hand side.

####  3.3.3 Research Design

Figure 3: The two main conditions. SR recipes and Non-SR recipes were present
in both conditions, but were treated differently. Non-SR recipes were never
boosted, but SR recipes were boosted in the SR-boosting condition.

Recipe search results and individual presentations. In the two following main
conditions, users were first shown a sample search with a list of six search
results containing a mixture of three Non-SR recipes, and three SR recipes.
The search results were ordered from high to low value on the miniature taste
maps. Subsequently, the participants were guided through recipe presentation
pages for each search result in the list. On top of each recipe, the
participants were asked: “Please rate this recipe according to how it fulfills
your salty food preference on a scale from 1 to 7 (1 means very poorly and 7
means very well).” Participants could glean salty taste levels from the taste
map, the ingredients, as well as the nutritional info section’s sodium value
and percentage of daily recommended intake.

Baseline Condition. This was one of two main conditions in the experiment. In
this condition a search for either “chicken” or “pork” was shown, the opposite
term from that in the other main condition. Search results were ranked
according to the recipes’ sodium score (as opposed to SR-boosting score),
displayed on the miniature taste maps. The participants were then guided
through the recipe presentations. Three of the six recipes contained SR
ingredients, but participants were not given any textual explanation about
this, and there was no boosting of the taste map value in search results or on
the recipe pages.

SR-Boosting Condition. This was the other main condition. A search for either
“chicken” or “pork” was presented, the opposite term from that in the baseline
condition. Search results were ranked according to the recipes’ SR-boosting
score as shown in Table 1. Three of the search results contained SR
ingredients and were boosted. This would most often make a visible difference
in the miniature taste map beside the search result and in the larger taste
map in the recipe presentation. In the presentation of SR recipes the taste
map was accompanied by a textual explanation about the presence of SR
ingredients. For example the headline: “Healthy ingredients enhancing salty
taste”, and text: “Garlic joins the salt in bringing out the flavor of the
ingredients”. The SR-boosting condition only boosted SR recipes. Non-SR
recipes did not change, had no boosted taste map value and no textual
explanation. Although the presentation of Non-SR recipes did not change across
conditions, they were present in the search result sets so that each set
mirrored the mixture of recipe types typically found in a real world search
results.

####  3.3.4 Procedure

Figure 4: Full procedure of the online study including the within-subjects
research design. In the sequence of two searches, the search result lists
could be sorted by either sodium score or SR-boosting score and corresponding
miniature taste maps were shown. In individual recipe presentations following
each search result list, presentations of SR recipes were sometimes boosted
with an explanation and a taste map value based on SR-boosting score. Boosting
is marked with italics.

Figure 4 shows that after initial questions the participants were randomly
assigned to four branches, ending up with a questionnaire. There were two main
conditions with randomized order. These were paired with the two search
samples, which also had randomized order. The visual scores in the taste maps
were based on sodium scores in the baseline condition and SR-boosting scores
in the SR-boosting condition. The latter condition also showed a textual
explanation accompanying a boosted taste map value when a recipe had SR
ingredients.

Initially in the procedure, participants were asked about characteristics
including cooking experience and healthy eating habits. They were then shown a
static page with an example of how a search in our recipe site could look.
Either the search for “chicken” or “pork” was shown together with the
corresponding recipe set consisting of six search results. Participants were
asked to go through each recipe, read the recipe presentation, and rate it
according to how well it satisfied their salty food preference. A second
search sample showed the remaining search term, either “chicken” or “pork”,
and the resulting recipe set. Once more, participants were asked to go through
the recipes of the search results and rate them. Finally, participants filled
out a short questionnaire where they were asked about their attitude towards
SRs.

####  3.3.5 Measures

The answers to the questions participants were asked before the experiment
were used as independent variables later in the analysis. We inquired about
their age and gender (Male, Female, Other). Then, about what their highest
completed education was: Less than high school, High school or equivalent,
Bachelor degree (e.g. BA, BSc), Master degree, Doctorate, or Prefer not to
say. They were also asked about their cooking experience and eating habits on
a 5-point scale from “Very low / Very unhealthy” to “Very high / Very
healthy”.

In the experiment we collected user preference data used as dependent variable
in the analysis. Each participant was shown a total of 12 full recipe
presentations. For each recipe the user was asked to read the presentation and
rate the recipe according to how it fulfilled their salty-food preference on a
7-point Likert scale from “Very poorly” to “Very well”.

The questionnaire after the experiment notably asked a control variable Likert
scale question: “Research shows that many people can be just as satisfied if
salt is reduced a little and garlic, oregano or rosemary added. To what extent
do you think that you would be satisfied with this in the future on a scale
from 1 (to no extent) to 7 (to a high extent)?”

##  4 Results

In this section we outline the main results from the online study, regarding
our two research questions.

###  4.1 RQ1: Food Preferences & Salt Replacement Explanations

We first examined whether user preferences for SR recipes changed due to our
taste map value boost and textual explanation (RQ1). Overall, 200 participants
gave 2x 600 ratings for Non-SR recipes and 2x 600 ratings for SR recipes
across the two main conditions. Since the boosting-explanations were only
presented alongside recipes that actually contained SR ingredients, we
performed a dependent t-test that compared the 2x 600 ratings for SR recipes.
The t-test showed that ratings given in the SR-boosting condition ($M=4.54$,
$SD=1.77$) were significantly higher than in the baseline condition ($M=4.33$,
$SD=1.79$): $t$(1198) = 2.08, $p<0.05$.

It is easier to understand this result by inspecting Figure 5. While there was
no difference across conditions for Non-SR ingredients, the rating given for
SR recipes increased around .20 on a scale from 1 to 7. This either suggested
that explanations boosted preferences for SR recipes or that they stood out
from the larger list of recipes.

Figure 5: Marginal effects on the ratings for two recipe types with and
without salt-replacer ingredients (e.g. oregano) across both conditions (based
on sodium score or SR-boosting score). Bars represent 95%-CIs.

###  4.2 RQ2: Recipe & User Characteristics

We further examined whether other recipe features and user characteristics
affected users’ food preferences with regard to salty taste. To do so, we
predicted the rating given by users to the presented recipes, based on a
recipe’s taste value, a user’s self-reported health with regard to her eating
habits, the SR-boosting condition, cooking experience and a user’s attitude
towards SR ingredients. Table 2 describes the two random effects models. Model
1 shows that recipes for which a higher taste map value is reported, whether
based on sodium score or SR-boosting score, are also more likely to receive a
higher rating: $\beta=1.34$, $p<0.001$. This confirmed our expectations that a
recipe’s salty taste is an important predictor of expected food enjoyment and
food preferences.

Table 2: Two multilevel regression models predicting the rating given by a
user to a presented recipe. Model 1 only examines the relation between the
salty taste value and rating, while Model 2 includes more user characteristics
and the effects due to the SR-boosting condition. ${}^{***}p<0.001$,
${}^{**}p<0.01$, ${}^{*}\leavevmode\nobreak\ p\leavevmode\nobreak\
<\leavevmode\nobreak\ 0.05$.

Factor | Model 1 | Model 2
---|---|---
|  $\beta$ (S.E.) |  $\beta$ (S.E.)
Presented Taste Map Value | 1.34 (.12)∗∗∗ | 2.64 (.50)∗∗∗
Self-reported Health |  | .46 (.12)∗∗∗
Recipe Rating X Self-Health |  | -.37 (.14)∗∗
Salt-Replacer Attitude |  | .19 (.057)∗∗
SR-Boosting Condition |  | .52 (.34)
(Vs Baseline) |  |
Condition X Self-Health |  | -.068 (.074)
Condition X Attitude |  | -.046 (.047)
Cooking Experience |  | .050 (.080)
Age |  | -.0045 (.0051)
Constant | 3.72 (.095)∗∗∗ | 1.08 (.49)∗
${\chi}^{2}$ | 125.04∗∗∗ | 158.44∗∗∗
${\rho}$ | 0.25 | 0.23
$R^{2}$ | 0.038∗∗∗ | 0.072∗∗∗

Table 2, Model 2 expands our baseline model by adding user characteristics, as
well as by controlling for our SR-boosting condition. We again observed a
positive relation between the presented taste map value and the given rating
($beta=2.68$, $p<0.001$), further confirming our expectations. In addition,
Table 2 shows that users with self-reported health were more likely to give a
higher rating: $\beta=.46$, $p<0.01$, which suggested that users who perceived
themselves as having a healthy lifestyle still preferred foods that contained
higher amounts of salt. However, we also observed an interaction effect
between the taste map value and self-reported health, which was understood
best by inspecting Figure 6. While both higher levels of self-reported health
and a recipe’s taste map value positively affected the rating given, the
differences in slopes suggested that the increase was stronger for users with
lower self-reported health.

Figure 6: Given rating for presented recipes, based on the presented taste map
value and a user’s self-reported health. Bars represent 95%-CIs.

Table 2 also reports on other user characteristics. We found no relation
between a user’s cooking experience and the given rating ($p>0.05$), nor did
we observe an effect due to age ($p>0.05$). In contrast, we did find that a
user’s attitude towards SR ingredients positively affected the rating given to
recipes ($p<0.01$). Although it seemed sensible that this would mostly apply
to recipes that contained such SR ingredients, we observed no interaction
effect with the presented taste map value and the SR-boosting condition. Note
that analyzing the models in Table 2 only on recipes that contained SR
ingredients led to similar results, except that the interaction effect between
taste map value and self-reported health was non-significant.

##  5 Summary & Future Work

For clarity, we summarize our main findings:

  * •

We have examined to what extent food preferences change due to visual and
textual explanations on salty taste. We have found that SR recipes can achieve
a higher satisfaction of salty food preferences when boosted in the search
interface.

  * •

We have also examined to what extent other recipe and user characteristics can
affect salty food preferences. Recipes’ salty taste appears to be an important
predictor of expected food enjoyment, since recipes with a high displayed
salty taste value received the highest ratings for fulfilling salty food
preferences.

  * •

Users with a high level of self-reported health and those with a positive
attitude towards SRs provided higher ratings across all recipes, indicating a
higher overall expected enjoyment of food.

  * •

We have found evidence that participants with low levels of self-reported
health can benefit the most from SR explanations, since they had the largest
increase in given ratings.

A number of limitations have to be noted. Due to the novelty of the research
domain, our findings have to be interpreted with some caution. Hence, this is
one of the first studies on SR ingredients and salty food preferences in a
search interface. First of all, there were only three SR ingredients available
in our dataset sub-sample, namely garlic, oregano and rosemary. Also, we
conducted our study on a very limited number of recipes. A larger study is
obviously needed that captures a bigger variety of recipes and SR ingredients.

The study at hand investigated only short-term behavioral change. A longer-
term (weeks or months) experiment is needed to justify whether these changes
in behavior can be cemented [13].

Furthermore, we did not personalize the search results or provide results
based on specific salty food preferences of users. As a first effort, we
concentrated on studying the overall effect of boosting SR recipes for the
participant population, regardless of initial salty food preferences of
individuals. Further research is needed to also understand the effect of
personalization [4].

Future research should look into other axes of the taste map as presented in
this paper and especially user goals/dietary restrictions. Sweetness is, for
example, another interesting axis that may be worthwhile to explore further,
as high sugar intake also increases health risks. Finally, more research is
required to further examine how to boost healthier recipes in search
interfaces by means of novel interface interventions, without sacrificing user
satisfaction [1].

###### Acknowledgements.

This research is in parts funded by MediaFutures partners and the Research
Council of Norway (grant numbers 309339 and 310468).

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##  Appendix A Online Resources

The prototype source code and Stata dataset for the online experiment is
available at https://git.app.uib.no/foodrecsys/salty-food-preferences. The
prototype code is an adaptation of Martijn Willemsen’s MouseLabWeb.