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Found 5 papers, 2 papers with code
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation
In this paper, we propose a structural causal model-based method to address the popularity bias issue for sequential recommendation model learning.
Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control
As pairwise learning suits well for the ranking tasks, the previously proposed unbiased pairwise learning algorithm already achieves state-of-the-art performance.
Unbiased Learning to Rank with Biased Continuous Feedback
It is a well-known challenge to learn an unbiased ranker with biased feedback.
Unbiased Pairwise Learning to Rank in Recommender Systems
To provide personalized high quality recommendation results, conventional systems usually train pointwise rankers to predict the absolute value of objectives and leverage a distinct shallow tower to estimate and alleviate the impact of position bias.
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Moreover, through extensive experiments across SOTA MTL models, we have observed an interesting seesaw phenomenon that performance of one task is often improved by hurting the performance of some other tasks.
