Simulations for novel problems in recommendation: analyzing misinformation and data characteristics

TL;DR

This paper discusses the use of simulation methods in recommender systems to analyze misinformation spread and data characteristics, highlighting their potential for future research advancements.

Contribution

It introduces how simulation approaches can be applied to study misinformation and data effects in recommendation systems, proposing future research directions.

Findings

Simulation helps analyze misinformation spreading.

Data characteristics significantly impact recommendation performance.

Future work can leverage simulation for deeper insights.

Abstract

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.