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Found 5 papers, 0 papers with code
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
While previous studies have demonstrated the effectiveness of using user behavior signals (e. g., clicks) as both features and labels of LTR algorithms, we argue that existing LTR algorithms that indiscriminately treat behavior and non-behavior signals in input features could lead to suboptimal performance in practice.
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance
Classification of large multivariate time series with strong class imbalance is an important task in real-world applications.
Scalable Feature Selection for (Multitask) Gradient Boosted Trees
Gradient Boosted Decision Trees (GBDTs) are widely used for building ranking and relevance models in search and recommendation.
Aggregated Customer Engagement Model
In this paper, we present the offline and online analysis and results comparing the individual and aggregated customer engagement models trained on e-commerce data.
Extended Missing Data Imputation via GANs for Ranking Applications
We prove that the optimal GAN imputation is achieved for Extended Missing At Random (EMAR) and Extended Always Missing At Random (EAMAR) mechanisms, beyond the naive MCAR.
