This paper proposes a personalized learning resource recommendation method based on dynamic collaborative filtering algorithm. Pearson correlation coefficient is used to calculate the data similarity between learning users or project resources in the network, and the unscored value is obtained. In order to solve the problems of sparse data and poor scalability in collaborative filtering algorithm, dynamic k-nearest-neighbor and Slope One algorithm are used to optimize it, and the sparsity of learning resource data in the network is analyzed according to the result of neighbor selection. The bidirectional self-equalization of stage evolution is used to improve the personalized recommendation of resource push, and the fuzzy adaptive binary particle swarm optimization algorithm based on the evolution state judgment is used to solve the problem of the optimal sequence recommendation, so as to realize the personalized learning resource recommendation. The experimental results show that the proposed method has higher matching degree and faster recommendation speed.

提出了一种基于动态协同过滤算法的个性化学习资源推荐方法。皮尔逊相关系数用于计算网络中学习用户或项目资源之间的数据相似度，并获得非计分值。为了解决协作过滤算法中数据稀疏，可扩展性差的问题，采用动态k近邻算法和Slope One算法对其进行优化，并根据结果对网络中学习资源数据的稀疏性进行了分析。邻居选择。阶段演化的双向自均衡用于改善资源推送的个性化推荐，基于进化状态判断的模糊自适应二进制粒子群算法，解决了最优序列推荐问题，实现了个性化学习资源的推荐。实验结果表明，该方法具有较高的匹配度和较快的推荐速度。