  Interactive recommender systems that enable the interactions between users
and the recommender system have attracted increasing research attentions.
Previous methods mainly focus on optimizing recommendation accuracy. However,
they usually ignore the diversity of the recommendation results, thus usually
results in unsatisfying user experiences. In this paper, we propose a novel
diversified recommendation model, named Diversified Contextual Combinatorial
Bandit (DC$^2$B), for interactive recommendation with users' implicit feedback.
Specifically, DC$^2$B employs determinantal point process in the recommendation
procedure to promote diversity of the recommendation results. To learn the
model parameters, a Thompson sampling-type algorithm based on variational
Bayesian inference is proposed. In addition, theoretical regret analysis is
also provided to guarantee the performance of DC$^2$B. Extensive experiments on
real datasets are performed to demonstrate the effectiveness of the proposed
method.
