35 citations · 124 across the 10 of their papers we have counts for
3 papers · 1 filter
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…
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…
A Click Sequence Model for Web Search
Alexey Borisov, Martijn Wardenaar, Ilya Markov +1
Getting a better understanding of user behavior is important for advancing information retrieval systems. Existing work focuses on modeling and predicting single interaction events…