Presentation at the Explainable User Models and Personalized Systems (ExUM), Adjunct to the 2021 ACM UMAP conference. The full paper is available here: https://dl.acm.org/doi/10.1145/3450614.3464477

1. Using Explanations as Energy-Saving Frames: A
User-Centric Recommender Study
Alain Starke, Wageningen University & Research, NL
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Martijn Willemsen & Chris Snijders, Eindhoven University of Technology, NL

2. Main problem: promotion of energy conservation
at home
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3. Recommendation Problem:
Energy-saving measures differ in terms of how difficult they are to do
Domain of household energy conservation
3

4. Energy-saving measures can be ordered as increasingly difficult behavioral
steps towards attaining the goal of saving energy
(Kaiser et al., 2010; Urban & Scasny, 2014)
< <
Domain of household energy conservation
4

5.  What should be recommended to users to support energy savings?
● Past preferences might not reflect future needs
 How should energy-saving measures be recommended to users to support
energy savings?
● People often lack a proper understanding of what ‘kWh savings’ are
● Some attributes are important attributes: in particular perceived effort
Two main issues
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6.  To what extent can we promote energy-efficient decisions in a recommender
system by framing energy-saving measures in terms of their kWh
savings and other relevant attributes?
Research Question
6

7. Method
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8. Recommendation
Approach

9. Performed rarely
(1% of users)
Performed often
(99% of users)
Psychometric scale based on
the Rasch Model
This order of measures is
rather consistent across
different populations
Recommendation Approach

10. 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
ability

11. 5%
The position on the
scale serves as a
starting point for
energy-saving
recommendations
Probability of
performing a measure:
90%
50%
75%
25%
12%
95%
2%

12. 1. Users disclose self-reported behavior
2. Choose any number of measures
List of 20
3. Evaluate their experience
Procedure & Interface
12

13.  Baseline: Match score (based on the Rasch model)
 A Savings Score based on kWh savings
 A Smart Savings score based on kWh savings + perceived effort
Between-user design: 3 different frames
13

14.  Chosen kWh savings per measure + no. of chosen measures
 Number of measures inspected (hovers)
 User evaluation aspects
● Perceived support
● Choice satisfaction
● Domain knowledge
Measures
14

15. Results
15

16. Results – Likelihood that a measure with high
kWh savings was chosen increased
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17. Results - User Evaluation Framework
17

18. Results - User Evaluation Framework
18

19. Results - User Evaluation Framework
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20.  Study that combined psych theory, HCI, and recommender systems
 Framing intervention (nudge) applied in a personalized context
● Did not increase the amount of savings chosen
● But affected what measures were chosen in our Framing conditions
 Weak evidence for user evaluation benefits in the framing conditions
 Future work: Going beyond self-reported data using smart meters?
Concluding remarks
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21. Thank you for
your attention!