29 citations · 66 across the 11 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
cs.IR2018★ 4 cited
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Chang Li, Ilya Markov, Maarten de Rijke +1
Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaving methods, the problem of how to effe…
cs.LG2018★ 9 cited
Incremental Sparse Bayesian Ordinal Regression
Chang Li, Maarten de Rijke
Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches…
cs.LG2018★ 14 cited
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Chang Li, Branislav Kveton, Tor Lattimore +4
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally be…