TY - Jour A2 - Versaci，Mario Au - Jin，Canghong Au - 周，Yuli Au - ying，胜宇奥 - 张，志奥 - 王，Weisong Au - Wu，Minghui Py - 2020 DA - 2020/12/23 Ti -具有关注网络的知识融合排名系统，用于制作分配建议SP - 6748430 VL - 2020 AB - 近几十年来，更多的教师正在使用问题发生器，为学生提供在线作业。学习 - 排名（LTR）方法可以部分排名以满足个别学生的需求，减少他们的学习负担。Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students’ latent knowledge and cognitive level is difficult, (2) the content of quizzes can be totally different but the knowledge points of these quizzes may be inherently related, and (3) ranking models based on supervised, semisupervised, or reinforcement learning focus on the current assignment without considering past performance. In this work, we propose KFRank, a knowledge-fusion ranking model based on reinforcement learning, which considers both a student’s assignment history and the relevance of quizzes with their knowledge points. First, we load students’ assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student’s knowledge cognitive and the question. Then, a similarity estimator is built to choose historical questions, and an attention neural network is used to calculate the attention value and update the current study state with knowledge fusion. Finally, a rank algorithm based on a Markov decision process is used to optimize the parameters. Extensive experiments were conducted on a real-life dataset spanning a year and we compared our model with the state-of-the-art ranking models (e.g., ListNET and LambdaMART) and reinforcement-learning methods (such as MDPRank). Based on top- K. NDCG值，我们的模型优于平均水平和弱者组的其他方法，其研究能力相对较差，因此他们的行为更难以预测。SN - 1687-5265 UR - HTTPS://Doi.org/10.1155/2020/6748430 Do - 10.1155 / 2020/6748430 JF - 计算智能和神经科学PB - Hindawi Kw - ER -