MOOC Course Recommendation Method Based on Implicit Feedback and Deep Reinforcement Learning
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.
万方数据
维普
