Can we leverage rating patterns from traditional users to enhance recommendations for children?

TL;DR

This paper explores whether rating data from adult users can be used to improve recommendation systems for children, addressing data scarcity issues in child-centric recommendations.

Contribution

It presents an initial analysis on leveraging adult user data to enhance child recommendation algorithms, a novel approach in addressing data scarcity for children.

Findings

Adult rating patterns can provide valuable insights for child recommendations.

Leveraging traditional user data can partially mitigate data scarcity for children.

Further research is needed to optimize transfer of rating patterns across age groups.

Abstract

Recommender algorithms performance is often associated with the availability of sufficient historical rating data. Unfortunately, when it comes to children, this data is seldom available. In this paper, we report on an initial analysis conducted to examine the degree to which data about traditional users, i.e., adults, can be leveraged to enhance the recommendation process for children.