Deconfounded Recommendation for Alleviating Bias Amplification
Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua
Abstract
Recommender systems usually amplify the biases in the data. The model learned from historical interactions with imbalanced item distribution will amplify the imbalance by over-recommending items from the majority groups. Addressing this issue is essential for a healthy ecosystem of recommendation in the long run. Existing work applies bias control to the ranking targets (e.g., calibration, fairness, and diversity), but ignores the true reason for bias amplification and trades off the recommendation accuracy.
Topics & Concepts
Recommender systemComputer scienceRanking (information retrieval)Control (management)Information biasArtificial intelligenceMachine learningData miningWork (physics)Data scienceInformation retrievalDistribution (mathematics)Recommender Systems and TechniquesAdvanced Bandit Algorithms ResearchAdvanced Graph Neural Networks