Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot
This addresses personalized educational recommendations for children through interactive robots, though it appears incremental with domain-specific optimizations.
The authors tackled personalized book recommendation for children in a robot interaction environment by developing a text search algorithm, user interest prediction method, and synonym association technique, achieving improved performance while operating on resource-limited embedded devices.
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.