基于序列感知与多元行为数据的MOOCs知识概念推荐OA北大核心CSTPCD

In massive open online courses(MOOCs),knowledge concept recommendation aims to analyze and extract learning records from a platform to recommend personalized knowledge concepts to users,thereby avoiding the inefficiencies caused by the blind selection of learning content.However,existing methods often lack comprehensive utilization of the multidimensional aspects of user behavior data,such as sequential information and complex interactions.To address this issue,we propose STRec,a sequence-aware and multi-type behavioral data driven knowledge concept recommendation method for MOOCs.STRec extracts the sequential information of knowledge concepts and combines it with the features produced by graph convolutional networks using an attention mechanism.This facilitates the prediction of a user's next knowledge concept of interest.Moreover,by employing multi-type contrastive learning,our method integrates user-interest preferences with various interaction relationships to accurately capture personalized features from complex interactions.The experimental results on the MOOCCube dataset demonstrate that the proposed method outperforms existing baseline models across multiple metrics,validating its effectiveness and practicality in knowledge concept recommendation.