Recommendation systems in the scope of opinion formation: a model

TL;DR

This paper introduces a social opinion formation model on complex networks to simulate fat-tailed distributions in recommendation system data, aiding in understanding and testing recommender systems.

Contribution

A novel model based on social interactions and opinion dynamics that replicates real-world data distributions in recommender systems.

Findings

Model fits real attendance data distributions

Mathematically analyzed via master equation

Provides a basis for generating artificial data sets

Abstract

Aggregated data in real world recommender applications often feature fat-tailed distributions of the number of times individual items have been rated or favored. We propose a model to simulate such data. The model is mainly based on social interactions and opinion formation taking place on a complex network with a given topology. A threshold mechanism is used to govern the decision making process that determines whether a user is or is not interested in an item. We demonstrate the validity of the model by fitting attendance distributions from different real data sets. The model is mathematically analyzed by investigating its master equation. Our approach provides an attempt to understand recommender system's data as a social process. The model can serve as a starting point to generate artificial data sets useful for testing and evaluating recommender systems.