Rank List Sensitivity of Recommender Systems to Interaction Perturbations. (arXiv:2201.12686v3 [cs.IR] UPDATED)

Prediction models can exhibit sensitivity with respect to training data:
small changes in the training data can produce models that assign conflicting
predictions to individual data points during test time. In this work, we study
this sensitivity in recommender systems, where users' recommendations are
drastically altered by minor perturbations in other unrelated users'
interactions. We introduce a measure of stability for recommender systems,
called Rank List Sensitivity (RLS), which measures how rank lists generated by
a given recommender system at …

ir recommender systems systems