The majority of recommender systems require explicit user interaction (ranking of movies and TV programmes and/or their metadata, such as genres, actors etc), which requires user time and effort. Furthermore, such ranking is often done separately by each person, while merging these manually acquired individual preferences in multi-user environments remains largely an unsolved problem. This work presents a method for learning a joint model of a multi-user environment from implicit interactions: programme choices which family members make together and separately. The proposed method allows to adapt to the practices of each particular family and to protect family privacy, because the joint family model is learned for each family separately. Furthermore, since the accuracy of machine learning methods is family-dependent and none of the machine learning methods outperforms others for all families, a fairly lightweight classifier ensemble selection approach is applied for better adaptation to the specifics of each family. In tests on the real-life TV viewing histories of 20 families, acquired over 5 months, the classifier ensemble achieved an accuracy comparable with that of systems which require explicit user ratings: an average recall of 57% at an average precision of 30%, despite only a few programme metadata descriptors being available.
|Pages (from-to)||143 - 157|
|Number of pages||15|
|Publication status||Published - 2009|
|MoE publication type||A1 Journal article-refereed|
- intelligent systems
- TV recommender system
- user modelling