Traditional course recommendation systems use historical interaction data to evaluate user preferences for items and make recommendations accordingly.However,the acquisition of fine-grained and dynamic user preferences is limit-ed,resulting in inaccurate and uninterpretable recommendation results.To address this issue,this paper proposes a MOOC course recommendation method based on deep reinforcement learning with implicit feedback(MCIFRL),which aims to im-prove the accuracy and interpretability of recommendation results.The method employs a multi-layer perceptron model to build a deep reinforcement learning decision network,extracting user conversation history and candidate item quantity infor-mation,to generate actions to inquire about attributes or recommend items.Subsequently,through question-and-answer inter-action,the attributes of the item itself are inquired about multiple times to achieve accurate recommendations.The experiment generates a recommendation list based on the system's prediction of the user's score for the items and validates it on the pub-lic MoocData dataset.The results show that compared to other methods,MCIFRL has improved the accuracy of recommenda-tion results at different recommendation list lengths,and has improved both HR and NDCG indicators.
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