Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot

TL;DR

This paper presents a personalized children's book recommender system integrated with a real-time interactive robot, utilizing novel algorithms for text search, interest prediction, and understanding user intent, optimized for embedded devices.

Contribution

It introduces new algorithms for efficient text search, interest prediction, and synonym association tailored for child-robot interactions, enhancing recommendation accuracy and system performance.

Findings

Improved recommendation accuracy demonstrated in experiments

System operates efficiently on embedded devices

Enhanced understanding of children's fuzzy language inputs

Abstract

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.