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Non-stationarity appears in many online applications such as web search andadvertising. In this paper, we study the online learning to rank problem in anon-stationary environment where user preferences change abruptly at an unknownmoment in time. We consider the problem of identifying the K most attractiveitems and propose cascading non-stationary bandits, an online learning variantof the cascading model, where a user browses a ranked list from top to bottomand clicks on the first attractive item. We propose two algorithms for solvingthis non-stationary problem: CascadeDUCB and CascadeSWUCB. We analyze theirperformance and derive gap-dependent upper bounds on the n-step regret of thesealgorithms. We also establish a lower bound on the regret for cascadingnon-stationary bandits and show that both algorithms match the lower bound upto a logarithmic factor. Finally, we evaluate their performance on a real-worldweb search click dataset.

Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model