An unsupervised recommender system for smart homes

  • Authors:
  • Katharina Rasch

  • Affiliations:
  • KTH Royal Institute of Technology, School of Information and Communication Technology, 16440 Stockholm, Sweden. E-mail: krasch@kth.se

  • Venue:
  • Journal of Ambient Intelligence and Smart Environments - Ambient and Smart Component Technologies for Human Centric Computing
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

Abstract

Inhabitants of today's smarter homes struggle with complicated user interfaces and inflexible home configurations. The proposed smart home recommender system addresses these issues by continuously interpreting the user's current situation and recommending services that fit the user's habits, i.e. automate some action that the user would want to perform anyway. With these recommendations it is possible to build much simpler user interfaces that highlight the most interesting choices currently available. Configuration becomes much more flexible, since the recommender system automatically learns user habits. Evaluations on two smart home datasets show that the algorithm produces correct recommendations with 61% and 73% accuracy, respectively.