An Adaptive Video Program Recommender Based on Group User Profiles
Recommender systems for personal preferences have been widely developed for applications such as Amazon online shopping website, pandora radio and netflix movie. They are developed based on recorded user preferences to estimate user ratings on new items/stuffs. To recommend TV or movies, it has to perform recommendation for group users. By simply merging preferences of group users, it can act as a single user for recommendation. However, this approach ignores individual user preferences, and user dominance in interaction, which cannot reflect practical group user interests. We proposed to estimate inter-user dominance factor through the neural network algorithm, based on practical group user rating records. In addition, both content-based and user-based collaborative filtering methods are adopted based on the inter-user dominant factors to predict group users’ preference for program recommendation. The proposed adaptive program recommender based on dynamic group user profiles is evaluated from practical experiments on Movielens user rating databases. In addition, an active face recognition function has been integrated with the recommender system to provide touchless and user-friendly user interface for a home TV program recommendation prototype. Experiments showed that the proposed method can achieve higher accuracy in recommending video programs for group users, in additional to user-friendly recommendation function.
Keywordscontent-based user-based collaborative filtering neural network
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