The Impact of a Popularity Punishing Hyperparameter on ItemKNN Recommendation Performance

Robin Verachtert, Jeroen Craps, Lien Michiels, Bart Goethals

Abstract: Collaborative filtering techniques have a tendency to amplify popularity biases present in the training data if no countermeasures are taken. The ItemKNN algorithm with conditional probability-inspired similarity function has a hyperparameter $$\alpha $$ that allows one to counteract this popularity bias. In this work, we perform a deep dive into the effects of this hyperparameter in both online and offline experiments, with regard to both accuracy metrics and equality of exposure. Our experiments show that the hyperparameter can indeed counteract popularity bias in a dataset. We also find that there exists a trade-off between countering popularity bias and the quality of the recommendations: Reducing popularity bias too much results in a decrease in click-through rate, but some counteracting of popularity bias is required for optimal online performance.