  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 relevance, while result diversification focuses on
generating a ranked list of items that covers a broad range of topics. In this
paper, we study an online learning setting that aims to recommend a ranked list
with $K$ items that maximizes the ranking utility, i.e., a list whose items are
relevant and whose topics are diverse. We formulate it as the cascade hybrid
bandits (CHB) problem. CHB assumes the cascading user behavior, where a user
browses the displayed list from top to bottom, clicks the first attractive
item, and stops browsing the rest. We propose a hybrid contextual bandit
approach, called CascadeHybrid, for solving this problem. CascadeHybrid models
item relevance and topical diversity using two independent functions and
simultaneously learns those functions from user click feedback. We conduct
experiments to evaluate CascadeHybrid on two real-world recommendation
datasets: MovieLens and Yahoo music datasets. Our experimental results show
that CascadeHybrid outperforms the baselines. In addition, we prove theoretical
guarantees on the $n$-step performance demonstrating the soundness of
CascadeHybrid.
