Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot
In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.
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