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In the era of information explosion, recommender systems play a critical role in resisting the information overload.

Through extensive experiments on three public datasets, we demonstrate that TRGIR-DDPG achieves state-of-the-art performance over several baselines in a time-efficient manner. In the TRGIR implementation with Deep Deterministic Policy Gradient (DDPG), denoted as TRGIR-DDPG, we design a policy vector, which can represent user's preferences, to generate discrete actions from the candidate set. Since the action space of IRS is discrete, it is natural to implement TRGIR with Deep Q-learning Network (DQN). Two types of representative reinforcement learning algorithms have been applied to implement TRGIR. Moreover, we design an effective method to construct an action candidate set, which reduces the scale of the action space directly. Specifically, we leverage textual information to map items and users into a same feature space by a self-supervised embedding method based on the graph convolutional network, which greatly alleviates data sparsity problem. To address these two problems, in this article, we propose a Text-based deep Reinforcement learning framework using self-supervised Graph representation for Interactive Recommendation (TRGIR). The utilization of recommendation-related textual knowledge can tackle this problem to some extent, but existing RL-based recommendation methods either neglect to combine textual information or are not suitable for incorporating it. Moreover, data sparsity is another problem that most IRSs are confronted with. However, most of the existing RL-based IRSs usually face large discrete action space problem, which severely limits their efficiency. It does not store any personal data.Due to its nature of learning from dynamic interactions and planning for long-run performance, Reinforcement Learning (RL) has attracted much attention in Interactive Recommender Systems (IRSs). The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. The cookies is used to store the user consent for the cookies in the category "Necessary". The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly.