An unsupervised recommender system for smart homes

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.

Anomalous behavior detection-based approach for authenticating smart home system users

“…Since it has been extensively used in many ambient intelligence applications such as SHS recommender systems [ 53 , 54 ], the history data of user physical activities inside a smart environment can be easily found in public repositories. To generate an IoT-specific dataset from non-IoT-specific ones, we propose to adapt the history data of manual operation of appliances and objects by inhabitants in real-world home environments and assume that this data belongs to a controlling app manipulation.…”

“…Since it has been extensively used in many ambient intelligence applications such as SHS recommender systems [53,54], the history data of user physical activities inside a smart environment can be easily found in public repositories.…”

Recommender Systems for the Internet of Things: A Survey

“…It is evaluated by two steps: (1) a simulated user to test the accuracy of the clustering user profiles and (2) real users to verify the whole system. In another experiment, Rasch et al [85] adapted unsupervised learning to build an RS for smart homes. The system learns user patterns and conducts recommendations based on user contexts.…”

Towards a social and context-aware mobile recommendation system for tourism

“…Due to the importance of the topic and the increasing applicability due to the expansion of internet penetration and use, recommender systems appear in various fields of application including movie recommendation studies [29], e-business recommendations [30], expert recommendation [31,32], smart homes management [33], financial recommendations [34,35], social networks [36] or consumer electronics [37] citing just some of the most relevant cases.…”