=Paper=
{{Paper
|id=Vol-2439/7-paginated
|storemode=property
|title=RecSys Challenges in achieving sustainable eating habits
|pdfUrl=https://ceur-ws.org/Vol-2439/7-paginated.pdf
|volume=Vol-2439
|authors=Alain Starke
|dblpUrl=https://dblp.org/rec/conf/recsys/Starke19a
}}
==RecSys Challenges in achieving sustainable eating habits ==
https://ceur-ws.org/Vol-2439/7-paginated.pdf
RecSys Challenges in achieving sustainable eating habits
Alain Starke
University of Bergen
Bergen, Norway
A.D.Starke@tue.nl
ABSTRACT change can be achieved in the longer run [1, 3]. Most studies are
limited to decision-making within an interface, while only few
Most food recommender systems successfully fit one’s current studies monitor users over a longer time period [9]. However,
appetite for different food items. However, one’s current straightforward, one-time web studies cannot tackle questions
preferences (e.g. I like high-carb foods) may conflict with a new that concern changing habits, as this involves complex lifestyle
dietary goal (e.g. I want to minimize my carb intake), which can aspects as social practices [2, 11]. Moreover, energy intervention
be detrimental to recommendation quality over time. Moreover, studies in a persuasion or HCI context show that treatment
choices in the short-term might not lead to behavioral change in effects can diminish quickly and may depend on the type of
the longer-term. This short position paper outlines a few incentives provided [2], as superficial interface changes and
challenges related to changing habits, and reports a small financial rewards only have short-lived effects.
analysis to show how the Rasch model could complement CF- Another challenge is that one’s current habits and preferences
based approaches in research on sustainable eating habits. may differ from one’s behavioral goals, such as when taking up a
new diet. Traditional algorithms may simply reinforce one’s
CCS CONCEPTS current behaviors (e.g. ‘perhaps you want a bag of crisps’), rather
• Informa on Systems Decision Support Systems than allowing users to discover new items that might have a
• Human‐centered Compu ng User Studies lower predictive accuracy, since they are at odds with one’s
current habits (e.g. recommending low-calorie food). Although
KEYWORDS tags may be able helpful for discovery of novel items [1],
recommender systems might need to forgo on their predictive
Behavioral Change; User Experience; Rasch Model; accuracy and focus on listening to users with regard to goal-
Sustainability; Ea ng habits setting [3]. In addition, how decision psychology, persuasion and
interface design can effectively complement such algorithms to
1 Introduction spur behavior change is currently an open question [12].
Recent data-driven studies have addressed the topic of
sustainable eating [1, 13]. Whereas some users employ 3 Proposed research on changing habits
recommender systems to acquire recipes that fit their appetite or Recent studies show key differences in the behavioral difficulty
contain certain nutrients [9, 12], ‘sustainable eaters’ have a dual between various food items or energy-saving measures and their
goal of fine dining at little environmental costs. To date, research resulting attractiveness [9, 10]. Whereas it is easy to achieve a
has focused on algorithmic development (e.g. using CF-based small behavioral change (e.g. eating two cookies a day instead of
RecSys), an approach that has proven its merit in domains where four), moving away from one’s current behavior is tricky. In a
there is little difference between items with regard to the similar vein, behaviors related to medication adherence seem to
behavioral thresholds to adopt or choose them, such as in movie vary in their execution difficulty [6], showing that fully sticking
recommender systems. However, we argue that simply to one’s prescriptions is too hard for most patients.
presenting items that resonate with one’s current habits and To take behavioral difficulty into account, Schäfer and
preferences might not be effective in the long run [7]. Willemsen [9] and Starke et al. [10] have used the psychometric
Rasch model to conceptualize nutrient intake and energy
2 Key challenges conservation as one-dimensional constructs. They show that
A number of recommender domains show that presenting one’s motivation to perform a behavior (referred to as: ability or
appropriate recommendations is only the first step of adoption attitude [5]) becomes apparent through the behavioral items one
[3, 4, 13]. Although food, health, and energy recommender already engages in. This results in high adoption probabilities for
systems present items that should ultimately result in behavioral items that are commonly performed (i.e. have a low behavioral
change [3, 8, 10], there has been little work on how behavioral difficulty, ‘popular’), while such probabilities are low for obscure
items (e.g. installing solar PV or optimizing fiber intake) [9, 10].
HealthRecSys’19, September 20, 2019, Copenhagen, Denmark. The interplay between a user’s ability and item difficulty
© 2019 Copyright for the individual papers remains with the authors. Use permitted allows a recommender system to sketch the path towards
under Creative Commons License Attribution 4.0 International (CC BY 4.0). behavioral change, particularly for the dual behavioral goal of
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HealthRecSys’19, September 20, 2019, Copenhagen, Denmark A.D. Starke
sustainable eating. The Rasch model suggests that it might be sustainable behavior, as both graphs show similar results. In
more effective to take small steps over time, that slowly increase contrast, the Rasch-based set changes from mostly low-effort
in behavioral difficulty and continuously match a user’s ability measures for low-ability users, to a mixed recommendation set
[5, 11]. This would differ from CF-based approaches, as it does for high-ability users, including high kWh savings.
not look users that have similar ‘bad’ habits, but relies on users
that can lead the way to habit improvement. 4 Conclusion
To exemplify the difference between a Rasch-based approach
The Rasch model used in Figure 1 is one example of how an
and CF-based approaches, we analyzed 7,551 dichotomous self-
algorithm can consider changes in habits over predictive
reports (‘yes’ or ‘no’) of 304 users to 134 energy-saving
accuracy [cf. 7]. However, it does not address the question how a
behaviors, including food measures. We predicted Top-10
user can effectively change his or her habits. The recommender
recommendation sets for both approaches. Rasch would predict
community should conduct more intervention studies over a
the highest score for measures whose behavioral costs match a
longer-term period, by not only focusing on optimizing the
user’s ability. For the CF-based approach, we trained a Rating
recommender’s predictive accuracy, but also by monitoring the
Prediction model in MyMediaLite, using matrix factorization and
user’s behavior. For example, while historical data could point
5-fold cross-validation, noting that the CF model was not very
out one’s current habits, other methods of elicitation should
accurate (RMSE = 0.49 under dichotomous data).
reveal one’s behavioral goals. A useful method would be to
conduct more studies using mobile devices, as it would allow
Top-10 RecLists per cluster - Low Ability researchers to have user dialogs at multiple time points.
8
ACKNOWLEDGMENTS
This work was made possible by funding from the Nielsen
Mean Frequency
6
Stensen Fellowship.
4
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