211 citations · 249 across the 8 of their papers we have counts for
8 papers
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback
Yiling Jia, Hongning Wang
Deep neural networks (DNNs) demonstrate significant advantages in improving ranking performance in retrieval tasks. Driven by the recent technical developments in optimization and…
Learning Neural Ranking Models Online from Implicit User Feedback
Yiling Jia, Hongning Wang
Existing online learning to rank (OL2R) solutions are limited to linear models, which are incompetent to capture possible non-linear relations between queries and documents. In thi…
Learning Neural Contextual Bandits Through Perturbed Rewards
Yiling Jia, Weitong Zhang, Dongruo Zhou +2
Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because…
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank
Yiling Jia, Hongning Wang
Online learning to rank (OL2R) has attracted great research interests in recent years, thanks to its advantages in avoiding expensive relevance labeling as required in offline supe…
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
Yiling Jia, Huazheng Wang, Stephen Guo +1
Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users. However, the required exp…
Active Collaborative Sensing for Energy Breakdown
Yiling Jia, Nipun Batra, Hongning Wang +1
Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes…
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees
Yiyi Tao, Yiling Jia, Nan Wang +1
Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to…
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
Nan Wang, Hongning Wang, Yiling Jia +1
Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfacti…