Measuring the Predictability of Recommender Systems using Structural Complexity Metrics

arXiv:2404.08829v1 Announce Type: cross
Abstract: Recommender systems (RS) are central to the filtering and curation of online content. These algorithms predict user ratings for unseen items based on past preferences. Despite their importance, the innate predictability of RS has received limited attention. This study introduces data-driven metrics to measure the predictability of RS based on the structural complexity of the user-item rating matrix. A low predictability score indicates complex and unpredictable user-item interactions, while a high predictability score reveals less …

abstract algorithms arxiv attention complexity cs.ir cs.it cs.lg curation data data-driven filtering importance math.it measuring metrics online content ratings recommender systems study systems type