Measuring the Predictability of Recommender Systems using Structural Complexity Metrics

ArXi:2404.08829v2 Announce Type: replace-cross Recommender Systems (RS) shape the filtering and curation of online content, yet we have limited understanding of how predictable their recommendation outputs are. We propose data-driven metrics that quantify the predictability of recommendation datasets by measuring the structural complexity of the user-item interaction matrix. High complexity indicates intricate interaction patterns that are harder to predict; low complexity indicates simpler, predictable structures.