Abstract
Majority of recommender systems require explicit user interaction (ranking of movies and TV programs and/or their metadata, such as genres, actors etc), which requires user time and effort. Furthermore, often such ranking is done separately by each person, while merging these manually acquired preferences in multi-user environments remains largely unsolved problem. This work presents a method to learn a model of multi-user environment in intelligent home from implicit interactions: the choices which family members make together and separately. In tests on TV viewing histories of twenty families, acquired during two months, the method has achieved prediction accuracy comparable with the accuracy of systems which require explicit user ratings: a set of TV programs, actually viewed during each test session (average set size was 2.2 programs per viewing session), was recommended among five top choices in 60% of cases on average, despite training on small data sets.KeywordsUser ModellingTV recommender systemSmart Home
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