Add to ORKGPeople can adopt many different energy-saving measures, but how can they be encouraged to take action? Recommender systems could offer a solution, but how recommender systems are used and perceived will depend on the level of knowledge people have regarding energy-saving measures. We test an energy-saving recommender system that uses Multi-Attribute Utility Theory (MAUT) to recommend energy-saving measures to its users. Across four experiments we test nine different preference elicitation methods for this system, and demonstrate that users' system satisfaction with each of these interfaces depends on whether they are an expert on energy-saving or a novice. Moreover, we show that system satisfaction is a driver of behavioral outcomes. In eff...
This paper compares five different ways of interacting with an attribute-based recommender system an...
Since electricity consumption of households in developing countries is dramatically increasing every...
Despite the variety of sensors that can be used in a smart home or office setup, for monitoring ener...
Recommender systems usually seek to cater to the preferences of a single user. However, societal iss...
Recommender systems usually seek to cater to the preferences of a single user. However, societal iss...
People often struggle to find appropriate energy-saving measures to take in the household. Although ...
Although there are numerous possibilities to save energy, conservation initiatives often do not tail...
The recent advances in artificial intelligence namely in machine learning and deep learning, have bo...
Recommender systems typically use collaborative filtering: information from your preferences (i.e. y...
How can recommender interfaces help users to adopt new behaviors? In the behavioral change literatur...
Engaging consumers in energy efficient behavior is challenging. Despite most consumers consistently ...
Feedback systems on energy consumption are being provided more and more worldwide. However, the succ...
This paper compares five different ways of interacting with an attribute-based recommender system an...
Since electricity consumption of households in developing countries is dramatically increasing every...
Despite the variety of sensors that can be used in a smart home or office setup, for monitoring ener...
Recommender systems usually seek to cater to the preferences of a single user. However, societal iss...
Recommender systems usually seek to cater to the preferences of a single user. However, societal iss...
People often struggle to find appropriate energy-saving measures to take in the household. Although ...
Although there are numerous possibilities to save energy, conservation initiatives often do not tail...
The recent advances in artificial intelligence namely in machine learning and deep learning, have bo...
Recommender systems typically use collaborative filtering: information from your preferences (i.e. y...
How can recommender interfaces help users to adopt new behaviors? In the behavioral change literatur...
Engaging consumers in energy efficient behavior is challenging. Despite most consumers consistently ...
Feedback systems on energy consumption are being provided more and more worldwide. However, the succ...
This paper compares five different ways of interacting with an attribute-based recommender system an...
Since electricity consumption of households in developing countries is dramatically increasing every...
Despite the variety of sensors that can be used in a smart home or office setup, for monitoring ener...