  Interactive recommendation that models the explicit interactions between
users and the recommender system has attracted a lot of research attentions in
recent years. Most previous interactive recommendation systems only focus on
optimizing recommendation accuracy while overlooking other important aspects of
recommendation quality, such as the diversity of recommendation results. In
this paper, we propose a novel recommendation model, named
\underline{D}iversity-promoting \underline{D}eep \underline{R}einforcement
\underline{L}earning (D$^2$RL), which encourages the diversity of
recommendation results in interaction recommendations. More specifically, we
adopt a Determinantal Point Process (DPP) model to generate diverse, while
relevant item recommendations. A personalized DPP kernel matrix is maintained
for each user, which is constructed from two parts: a fixed similarity matrix
capturing item-item similarity, and the relevance of items dynamically learnt
through an actor-critic reinforcement learning framework. We performed
extensive offline experiments as well as simulated online experiments with real
world datasets to demonstrate the effectiveness of the proposed model.
