Learning context-dependency of user interests in smart homes: Comparison between SVM and CBR

Personal information needs and multimedia preferences depend on the long-term user interests as well as on the current situation (context) of a person. For example, adults are mainly interested in toys’ advertisements when child’s birthday approaches; selection of videos to watch strongly depends on who are present in a room: parents with children or just adults. As capabilities of personal computers and smart environments for being aware of users’ contexts grow, the users’ willingness to set manually rules for context-based information and/or multimedia retrieval will decrease. Thus computers must learn to associate user contexts with information needs in order to collect and present information proactively. This work presents initial experiments with two reasoning methods: SVM (Support Vector Machines) and CBR (Case-Based Reasoning) to learn dependency of user preferences on context which we are aiming to recognise in smart homes. Experimental results show that each of the methods has own advantages, and combination of the recommendations provided by both methods achieves fairly high recall rate with acceptable precision.