Name
Abstract | ||
|---|---|---|
We address the problem of recommending an appliance usage schedule to the homeowner which balances between maximising total savings and maintaining sufficient user convenience. An important challenge within this problem is how to elicit the the user preferences with low intrusiveness, in order to identify new schedules with high cost savings, that still lies within the user's comfort zone. To tackle this problem we propose iDR, an interactive system for generating personalised appliance usage scheduling recommendations that maximise savings and convenience with minimal intrusiveness. In particular, our system learns when to stop interacting with the user during the preference elicitation process, in order to keep the bother cost (e.g., the amount of time the user spends, or the cognitive cost of interacting) minimal. We demonstrate through extensive empirical evaluation on real-world data that our approach improves savings by up to 35%, while maintaining a significantly lower bother cost, compared to state-of-the-art benchmarks. |
Year | Venue | Field |
|---|---|---|
2016 | IJCAI | Preference elicitation,Scheduling (computing),Computer science,Computer security,Intrusiveness,Human–computer interaction,Schedule,Artificial intelligence,Cognition,Machine learning |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ngoc Cuong Truong | 1 | 24 | 1.83 |
| Tim Baarslag | 2 | 242 | 23.57 |
| sarvapali d ramchurn | 3 | 2239 | 161.28 |
| Long Tran-Thanh | 4 | 309 | 37.69 |