1 citations · 1 across the 5 of their papers we have counts for
5 papers
Behavioral Feature Boosting via Substitute Relationships for E-commerce Search
Chaosheng Dong, Michinari Momma, Yijia Wang +2
On E-commerce platforms, new products often suffer from the cold-start problem: limited interaction data reduces their search visibility and hurts relevance ranking. To address thi…
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments
Tianchen Zhou, Jia Liu, Yang Jiao +4
Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years. Among the existing approaches for ONL2R, a…
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
Minghe Zhang, Chaosheng Dong, Jinmiao Fu +8
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…
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
Minghong Fang, Jia Liu, Michinari Momma +1
Today, recommender systems have played an increasingly important role in shaping our experiences of digital environments and social interactions. However, as recommender systems be…
Multi-objective Ranking via Constrained Optimization
Michinari Momma, Alireza Bagheri Garakani, Nanxun Ma +1
In this paper, we introduce an Augmented Lagrangian based method to incorporate the multiple objectives (MO) in a search ranking algorithm. Optimizing MOs is an essential and reali…