I present work on using explanatory nudges to support 'better' decision-making in recommender systems. I aim to help people to achieve their behavioral goals by providing relevant options in the short-term that are clearly explained to them.

1. Using Explanatory Nudges to Support ‘Better’
Decision-Making in Recommender Systems
Dr.ir. Alain Starke – Postdoc Personalized Nutrition, Wageningen University, NL;
Adjunct Associate Professor Information Science, University of Bergen, NOR
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2.  Helping (or persuading?) users to improve themselves
 Helping users to attain their goals
 Helping users to overcome barriers
Better Decision-Making
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3. Recommender systems may struggle in domains
where people change their behavior
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4. RecSys often reinforce current preferences,
not future goals
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eaten

5. RecSys often promote consumption of *any*
item
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6.  Develop RecSys that support ‘future goals’, so-called better decision-making
 Amidst personalized advice, support better decisions by changing the decision
context (choice architecture) to trigger predictable behavior: Nudging
Aims for this research
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7.  Use justifications and explanations as a nudge towards self-improvement
Aims for this research
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Tintarev, N., & Masthoff, J. (2015). Explaining recommendations:
Design and evaluation. In Recommender systems handbook (pp. 353-
382). Springer, Boston, MA.

8.  An example of an energy recommender system in which users can improve,
along with a normative explanation nudge (based on psychology)
 Examples of a food recommender system with justifications that aims to
support healthier recipe choices
This talk
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9. Changing one’s sustainable habits is hard, I said
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10. Changing one’s sustainable habits is hard
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11.  Starke, A., Willemsen, M., & Snijders, C. (2021). Promoting Energy-Efficient
Behavior by Depicting Social Norms in a Recommender Interface. ACM
Transactions on Interactive Intelligent Systems (TiiS), 11(3-4), 1-32.
● Also featured at the non-existent IUI, 2020
 Starke, A. D., Willemsen, M. C., & Snijders, C. C. (2020). Beyond “one-size-
fits-all” platforms: Applying Campbell's paradigm to test personalized energy
advice in the Netherlands. Energy Research & Social Science, 59, 101311.
Energy Recommender Systems
for Household Energy Conservation
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12. Discrepancy between what is desirable (kwh
savings) versus what is feasible (difficulty)
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13. Making sense of energy-saving measures
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14. 14
Performed rarely
(1% of users)
Performed often
(99% of users)
This is a ‘Rasch scale’
(a psychometric model)
This order of measures is
rather consistent across
different populations

15. 15
Performed
rarely
(1% of users)
Performed
often
(99% of users)
Performs many
measures
(99%)
Performs few
measures
(1%)
Persons
are
ordered
on the
same
scale:
Behavioral
Costs
Energy-
saving
attitude

16. 5%
The position on
the scale serves
as a starting point
for energy-saving
recommendations
Probability of already
performing a measure:
90%
50%
75%
25%
12%
95%
2%

17. Findings in 2 recommender
studies (N = 222; N = 288)
1. The ‘green’ strategy was
evaluated as more
satisfactory and led to
more choices
2. Users preferred
measures that fell just
below their position on
the scale
Government
strategy
The ‘easy
stuff’
Starke, A.D., Willemsen, M.C., Snijders, C. (2017). Effective user interface designs to increase energy-
efficiency behavior in a Rasch-based energy recommender system. Proceedings RecSys ’17, 65-73.

18. Different measures
at the same position
But… users ignored
kWh savings when
making a choice
Users were influenced
by how ‘effortful’
measures were

19. Possibilities to nudge?
 Highlighting kWh savings?
● Might encourage those who are interested / ignorant
 Showing what others do?
● But the most popular measures are the easiest ones
 We opted to develop social norm nudges that highlight specific peers

20. Case of towel re-use
 Traditionally: “Help save the environment by… ”
 Social norms: “Join our fellow guests in… ”
An explanation for Sustainable Behavioral Advice
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21.  Goldstein et al. (2008): Social messages led to higher compliance rates
compared to an environmental message
● Through emphasis on majority (75% of guests)
● Through specific peer groups (“hotel guests” or “guests in room 408”
more effective than “people” or “citizens”)
Peer-specific social explanations – based on a
psychological study
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22.  “75% of people re-uses their towel”
 “75% of people in this city re-use their towel”
 “75% of hotel guests...”
 “75% of guests in room 408...”
 “75% of Dutch men in this room...”
Global vs local normative explanations
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Global Norms
Local Norms

23.  A lot of people re-use their towel (social norms are useful (e.g., 75%))
● But does not apply to e.g. “Install Solar PV” (~20%)
 Towel reuse is once a day, has no costs, bears little effort
● Different from large-scale investments (perceived as effortful)
Towel reuse is just an example of an easy,
low-effort behavior
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24. 5%
Probability for
Person A:
90%
50%
75%
25%
12%
95%
2%
50%
99%
95%
98%
90%
75%
99.9%
25%
Probability for
Person B:
Normative Explanations can be Local

25. Social Norms: We add the compliance rate of
each behavior

26. Study design (online, N=207)
 KWh savings (0-100)
 Descriptive adoption probability (15%-75%)
 Adoption prob. of similar peer (20%-80%)
 Adoption prob. of expert peers (40%-90%)

27. Saving Scores did not affect within a list
what was chosen, norms did
0
.1
.2
.3
Proportion
chosen
0-20 20-40 40-60 60-80 80-100
Savings Score categories
Chosen measures per Score bin
0-20 20-40 40-60 60-80 80-100
Norm % categories
Chosen measures per Norm bin

28. Specific Norms improved feasibility

29. Feasibility was related to choosing more
measures

30. The nudges worked to a certain extent
 In one-size-fits-all contexts (or with towels), this had a large
effect. Not so many in a personalized interface
 Users were able to differentiate within lists, but no additional
benefits between lists

31. Food Domain

32. Food Domain
 Confusion about health content of foods / recipes
● Explanations that should explain item features
 Users may have goals they want to attain to
● Explanations that are persuasive or show the system
understands the user’s needs
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33.  Musto, C., Starke, A. D., Trattner, C., Rapp, A., & Semeraro, G. (2021). Exploring the
Effects of Natural Language Justifications in Food Recommender Systems. In
Proceedings of the 29th ACM Conference on User Modeling, Adaptation and
Personalization (pp. 147-157).
Explanations and Justifications in the Food
Domain
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34.  Affectively Oriented Nudges
● Making food look more attractive
 Behaviorally Oriented Nudges
● Re-ordering the options in a list on health
Translate findings from ‘offline’ nudges to the
online realm
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Cadario, R., & Chandon, P. (2020). Which healthy eating nudges work best? A meta-analysis of
field experiments. Marketing Science, 39(3), 465-486.

35. Affectively & Behaviorally food nudges
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Starke, A. D., Willemsen, M. C., & Trattner, C. (2021). Nudging healthy choices in food
search through visual attractiveness. Frontiers in Artificial Intelligence, 4, 621743.

36.  Affectively Oriented Nudges
● Making food look more attractive  food photos
 Behaviorally Oriented Nudges
● Re-ordering the options in a shelf  in a list
 Cognitively Oriented Nudges
● Food labels, highlighting specific aspects, etc.
Translate findings from ‘offline’ nudges to the
online realm
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Cadario, R., & Chandon, P. (2020). Which healthy eating nudges work best? A meta-analysis of
field experiments. Marketing Science, 39(3), 465-486.

37. Knowledge-based food recommender system
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Musto, C., Starke, A.D., Trattner, C., Rapp, A., & Semeraro, G. (2021). Exploring
the Effects of Natural Language Justifications in Food Recommender Systems.

38. Knowledge-based: Example from own study
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Musto, C., Starke, A.D., Trattner, C., Rapp, A., & Semeraro, G. (2021). Exploring
the Effects of Natural Language Justifications in Food Recommender Systems.

39.  Compared three conditions:
● No Justification
● Single Justification
● Pairwise Justification
 8 types of justifications
 N=504
Study Design
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40. Results
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41. To predict healthy choices
Results
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42.  Focusing on healthy foods only would lead to dissatisfaction, but
justifications (an informative nudge) could be used to retain
freedom of choice
 Supporting users with specific goals / motivations
Explanations in food
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43.  To what extent can nudging actually help in personalized interfaces?
 What would be the merit of personalized nudging or persuasion if the content
is also personalized?
 So far, I believe that nudging can help *to a certain, very much not well-
defined extent* in a personalized advice list
Open Questions
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44. Thank you!
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