A Knowledge Concept Recommendation Model Based on Tensor Decomposition and Transformer Reordering

Abstract

To help students choose the knowledge concepts that meet their needs so that they can learn courses in a more personalized way, thus improving the effectiveness of online learning, this paper proposes a knowledge concept recommendation model based on tensor decomposition and transformer reordering. Firstly, the student tensor, knowledge concept tensor, and interaction tensor are created based on the heterogeneous data of the online learning platform are fused and simplified as an integrated tensor; secondly, we perform multi-dimensional comprehensive analysis on the integrated tensor with tensor-based high-order singular value decomposition to obtain the student personalized feature matrix and the initial recommendation sequence of knowledge concepts, and then obtain the latent embedding matrix of knowledge concepts via Transformer that combine initial recommendation sequence of knowledge concepts and knowledge concept learning sequential information; finally, the final Top-N knowledge concept recommendation list is generated by fusing the latent embedding matrix of knowledge concepts and the students’ personalized feature matrix. Experiments on two real datasets show that the model recommendation performance of this paper is better compared to the baseline model.