  Recommendation is crucial in both academia and industry, and various
techniques are proposed such as content-based collaborative filtering, matrix
factorization, logistic regression, factorization machines, neural networks and
multi-armed bandits. However, most of the previous studies suffer from two
limitations: (1) considering the recommendation as a static procedure and
ignoring the dynamic interactive nature between users and the recommender
systems, (2) focusing on the immediate feedback of recommended items and
neglecting the long-term rewards. To address the two limitations, in this paper
we propose a novel recommendation framework based on deep reinforcement
learning, called DRR. The DRR framework treats recommendation as a sequential
decision making procedure and adopts an "Actor-Critic" reinforcement learning
scheme to model the interactions between the users and recommender systems,
which can consider both the dynamic adaptation and long-term rewards.
Furthermore, a state representation module is incorporated into DRR, which can
explicitly capture the interactions between items and users. Three
instantiation structures are developed. Extensive experiments on four
real-world datasets are conducted under both the offline and online evaluation
settings. The experimental results demonstrate the proposed DRR method indeed
outperforms the state-of-the-art competitors.
