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Found 6 papers, 1 papers with code
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity
Ensuring relevance quality in product search is a critical task as it impacts the customer’s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term.
Federated Multi-Objective Learning
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications.
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
In this paper, we propose a new approach called fair recommendation with optimized antidote data (FairRoad), which aims to improve the fairness performances of recommender systems through the construction of a small and carefully crafted antidote dataset.
Multi-Label Learning to Rank through Multi-Objective Optimization
Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e. g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue.
Multi-objective Ranking via Constrained Optimization
In this paper, we introduce an Augmented Lagrangian based method to incorporate the multiple objectives (MO) in a search ranking algorithm.
