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