Safe Collaborative Filtering

arXiv:2306.05292v2 Announce Type: replace-cross
Abstract: Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalized recommender systems to reduce the risk of losing users with low satisfaction. This study introduces a "safe" collaborative filtering method that prioritizes recommendation quality for less-satisfied users rather than focusing on the …

abstract algorithmic fairness arxiv class collaborative collaborative filtering cs.ir cs.lg dataset decision decision making fairness filtering machine machine learning making modern performance personalized recommender systems reduce risk samples success systems tasks type vital