Prediction of interface preferences with a classifier selection approach

Abstract

Interaction in smart environments should be adapted to the users’ preferences, e.g., utilising modalities appropriate for the situation. While manual customisation of a single application could be feasible, this approach would require too much user effort in the future, when a user interacts with numerous applications with different interfaces, such as e.g. a smart car, a smart fridge, a smart shopping assistant etc. Supporting user groups, jointly interacting with the same application, poses additional challenges: humans tend to respect the preferences of their friends and family members, and thus the preferred interface settings may depend on all group members. This work proposes to decrease the manual customisation effort by addressing the cold-start adaptation problem, i.e., predicting interface preferences of individuals and groups for new (unseen) combinations of applications, tasks and devices, based on knowledge regarding preferences of other users. For predictions we suggest several reasoning strategies and employ a classifier selection approach for automatically choosing the most appropriate strategy for each interface feature in each new situation. The proposed approach is suitable for cases where long interaction histories are not yet available, and it is not restricted to similar interfaces and application domains, as we demonstrate by experiments on predicting preferences of individuals and groups for three different application prototypes: recipe recommender, cooking assistant and car servicing assistant. The results show that the proposed method handles the cold-start problem in various types of unseen situations fairly well: it achieved an average prediction accuracy of $$72 \pm 1\,\%$$ . Further studies on user acceptance of predictions with two different user communities have shown that this is a desirable feature for applications in smart environments, even when predictions are not so accurate and when users do not perceive manual customisation as very time-consuming.