If their car talks to them, shall a kitchen talk too? Cross-context mediation of interaction preferences

So called "smart products" try to recognise user context and to deliver relevant information upon own initiative, e.g., to advise to buy a windscreen washing liquid or to stir an overheated meal. As variety of usage situations grow, it may become difficult for the users to configure interaction manually in every new case, e.g., to specify via which modalities to deliver different message types. This work proposes several strategies to predict interaction preferences of individual users and user groups for a new context, based on preferences of these and other users in other contexts and preferences of other users in the target context. In the experiments with the smart products' configurations, set by 21 test subjects for different contexts (new and known tasks in cooking and car servicing domains, performed alone and in a group), the best of the proposed preferences mediation strategies allowed to predict on average 75% of settings, chosen by individuals and groups