Personalized Recommender System for Children's Book Recommendation with A Real-time Interactive Robot

Authors

  • Yun Liu Fandou Information Technology Co. Ltd., China
  • Tianmeng Gao Fandou Information Technology Co. Ltd., China
  • Baolin Song Fandou Information Technology Co. Ltd., China
  • Chengwei Huang Fandou Information Technology Co. Ltd., China https://orcid.org/0000-0001-9060-6361

DOI:

https://doi.org/10.47852/bonviewJDSIS3202850

Keywords:

personalized search, word vectorization, recommender system, children's robot

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.

 

Received: 15 March 2023 | Revised: 11 April 2023 | Accepted: 28 April 2023

 

Ethical Statement:

This study does not contain any studies with human or animal subjects performed by any of the authors.

 

Conflicts of Interest:

The authors declare that they have no conflicts of interest to this work.

 

Data Availability Statement

The data that support the findings of this study are not publicly available due to involvement with commercial products.


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Published

2023-04-28

How to Cite

Liu, Y., Gao, T., Song, B., & Huang, C. (2023). Personalized Recommender System for Children’s Book Recommendation with A Real-time Interactive Robot. Journal of Data Science and Intelligent Systems. https://doi.org/10.47852/bonviewJDSIS3202850

Issue

Section

Research Articles

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