  Due to its nature of learning from dynamic interactions and planning for
long-run performance, reinforcement learning (RL) recently has received much
attention in interactive recommender systems (IRSs). IRSs usually face the
large discrete action space problem, which makes most of the existing RL-based
recommendation methods inefficient. Moreover, data sparsity is another
challenging problem that most IRSs are confronted with. While the textual
information like reviews and descriptions is less sensitive to sparsity,
existing RL-based recommendation methods either neglect or are not suitable for
incorporating textual information. To address these two problems, in this
paper, we propose a Text-based Deep Deterministic Policy Gradient framework
(TDDPG-Rec) for IRSs. Specifically, we leverage textual information to map
items and users into a feature space, which greatly alleviates the sparsity
problem. Moreover, we design an effective method to construct an action
candidate set. By the policy vector dynamically learned from TDDPG-Rec that
expresses the user's preference, we can select actions from the candidate set
effectively. Through experiments on three public datasets, we demonstrate that
TDDPG-Rec achieves state-of-the-art performance over several baselines in a
time-efficient manner.
