29 citations · 66 across the 11 of their papers we have counts for
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cs.LG2019★ 29 cited
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity
Chang Li, Haoyun Feng, Maarten de Rijke
Relevance ranking and result diversification are two core areas in modern recommender systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item r…
cs.LG2019★ 5 cited
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
Chang Li, Maarten de Rijke
Non-stationarity appears in many online applications such as web search and advertising. In this paper, we study the online learning to rank problem in a non-stationary environment…
cs.IR2019★ 4 cited
Online Learning to Rank with List-level Feedback for Image Filtering
Chang Li, Artem Grotov, Ilya Markov +1
Online learning to rank (OLTR) via implicit feedback has been extensively studied for document retrieval in cases where the feedback is available at the level of individual items.…