基于多维关联本体的学习资源推荐方法

A method of learning resource recommendation based on multidimensional correlation ontology

针对目前互联网环境下学习资源推荐方法无法满足用户垂直化、精准化以及个性化学习需求问题,探索融合学习资源维度、学习者维度以及情境维度的学习资源推荐方法。首先,构建学习资源推荐多维关联本体模型(MCOM),通过语义关系实现学习资源本体、学习者本体和情境本体关联;其次,设计动态自均衡二进制粒子群优化算法(DSEBPSO);最后,将MCOM本体模型与DSEBPSO算法融合应用,提出基于多维关联本体的学习资源推荐方法(MCOM-LROM),为学习者提供最优学习资源或学习路径。相较于当前主流的学习资源推荐方法,MCOM-LROM方法在推荐结果准确性、响应速度以及内容质量等方面性能更优。

Aiming at the problem that the current learning resource recommendation method in the internet environment cannot meet the user’s vertical,precise and personalized learning needs,the learning resource recommendation method that integrates the learning resource dimension,learner dimension and situation dimension is studied.First,a multi-dimensional correlation ontology model(MCOM)for learning resource recommendation is built,and the association of learning resource ontology,learner ontology and situation ontology through semantic relations is realized.Then,dynamic self-equilibrium binary particle swarm optimization algorithm(DSEBPSO)is designed.Finally,the MCOM ontology model and DSEBPSO algorithm are fused and applied.A multi-dimensional correlation ontology-based learning resource recommendation method(MCOM-LROM)is proposed.It can provide learners with optimal learning resources or learning paths.Compared with current mainstream learning resource recommendation methods,the MCOM-LROM method has better performance in terms of accuracy of recommendation results,response speed,and content quality.