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Aggregated data in real world recommender applications often featurefat-tailed distributions of the number of times individual items have beenrated or favored. We propose a model to simulate such data. The model is mainlybased on social interactions and opinion formation taking place on a complexnetwork with a given topology. A threshold mechanism is used to govern thedecision making process that determines whether a user is or is not interestedin an item. We demonstrate the validity of the model by fitting attendancedistributions from different real data sets. The model is mathematicallyanalyzed by investigating its master equation. Our approach provides an attemptto understand recommender system's data as a social process. The model canserve as a starting point to generate artificial data sets useful for testingand evaluating recommender systems.

Recommendation systems in the scope of opinion formation: a model