Recommendation systems in the scope of opinion formation: a model

Aggregated data in real world recommender applications of-ten 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 mech-anism 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 atten-dance distributions from different real data sets. The model is mathematically analyzed by investigating its master equa-tion. Our approach provides an attempt to understand rec-ommender 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