Safe Collaborative Filtering

Understand

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 average performance.
• Our approach minimizes the conditional value at risk (CVaR), which represents the average risk over the tails of users' loss.