The method of personalized learning materials recommendation based on multidimensional feature difference

Currently, in learning resources recommended field, researchers focus on collaborative filtering algorithm and binary particle swarm optimization (BPSO) algorithm. However, by using collaborative filtering algorithm, the learning resources are recommended with a too randomization way, which cannot meet the requirements of learners in building overall knowledge architecture. Furthermore, the recommended model based on BPSO algorithm asks to recommend the whole learning resources for all learners and the model data is hard to be predicted, which does not conform the development trend of intelligent online learning. In order to deal with the above problems, a personalized learning resources recommendation algorithm is proposed based on multidimensional feature differences. As a first step, learning resources recommended model is established according to the multidimensional feature differences in learners and learning resources, as well as the learning preferences. Next the collaborative filtering technology is adopted to predict model data. Finally, through combining the BPSO algorithm with collaborative filtering algorithm based on the multi-objective optimization characteristics of recommendation model, an adaptive binary particle swarm optimization algorithm is proposed to dynamically coordinate inertia weight and population diversity. As shown in the experiments, it is implemented that meeting the requirements in personalized learning resources recommendation with better precision.