Abstract
People often struggle to find appropriate energy-saving measures to take in the household. Although recommender studies show that tailoring a system's interaction method to the domain knowledge of the user can increase energy savings, they did not actually tailor the conservation advice itself. We present two large user studies in which we support users to make an energy-efficient behavioral change by presenting tailored energy-saving advice. Both systems use a one-dimensional, ordinal Rasch scale, which orders 79 energy-saving measures on their behavioral difficulty and link this to a user's energy-saving ability for tailored advice. We established that recommending Rasch-based advice can reduce a user's effort, increase system support and, in turn, increase choice satisfaction and lead to the adoption of more energy-saving measures. Moreover, follow-up surveys administered four weeks later point out that tailoring advice on its feasibility can support behavioral change.
Original language | English |
---|---|
Title of host publication | RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems, 27-31 August 2017, Como, Italy |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 65-73 |
Number of pages | 9 |
ISBN (Electronic) | 9781450346528 |
ISBN (Print) | 978-1-4503-4652-8 |
DOIs | |
Publication status | Published - Aug 2017 |
Event | 11th ACM Conference on Recommender Systems, RecSys 2017 - Como, Italy Duration: 27 Aug 2017 → 31 Aug 2017 Conference number: 11 |
Conference
Conference | 11th ACM Conference on Recommender Systems, RecSys 2017 |
---|---|
Abbreviated title | RecSys 2017 |
Country/Territory | Italy |
City | Como |
Period | 27/08/17 → 31/08/17 |
Keywords
- Behavioral change
- Energy conservation
- Rasch model
- Recommender systems
- User experience