3 citations · 4 across the 5 of their papers we have counts for
3 papers · 1 filter
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-Label Learning to Rank through Multi-Objective Optimization
Debabrata Mahapatra, Chaosheng Dong, Yetian Chen +2
Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically…
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…