Can we leverage rating patterns from traditional users to enhance recommendations for children?
This addresses the challenge of providing effective recommendations for children, who often lack sufficient data, by exploring cross-user data transfer, though it appears incremental as an initial analysis.
The paper tackled the problem of insufficient historical rating data for children in recommender systems by analyzing whether data from adult users could improve recommendations for children, finding initial evidence that such leveraging is possible.
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.