Simulations for novel problems in recommendation: analyzing misinformation and data characteristics
This is an incremental position paper for the recommendation community, focusing on applying existing simulation methods to new problems.
The paper discusses using simulation approaches to analyze misinformation spreading in recommender systems and understand data characteristics affecting algorithm performance, presenting future work directions.
In this position paper, we discuss recent applications of simulation approaches for recommender systems tasks. In particular, we describe how they were used to analyze the problem of misinformation spreading and understand which data characteristics affect the performance of recommendation algorithms more significantly. We also present potential lines of future work where simulation methods could advance the work in the recommendation community.