GEPR: Group-knowledge Enhanced Personalized Educational Resource Recommendation
TL;DR
Comparative experiments with nine recommendation methods show that GEPR can fully combine the semantic information of resources and group knowledge information, capture users’ personalized needs, and improve recommendation accuracy.
Abstract
To address the issues of sparse user interaction data and insufficient mining of knowledge features from the perspective of single users in online education platforms, a group-knowledge enhanced personalized educational resource recommendation model (GEPR) is proposed.First, a user-resource interaction graph is constructed based on multi-user interaction sequences, and user groups are iteratively partitioned through behavioral features.Next, the complex associations between resources and knowledge points are mined from text information, and a semantic information constraint term is designed to optimize the knowledge representation learning of resource entities, enhancing resource representation in both structural and textual dimensions.Then, a cross-attention mechanism with group knowledge constraints is designed to establish group-individual knowledge propagation channels, achieving the fusion of group and individual knowledge features through adaptive adjustment of attention weights.Finally, group knowledge information is integrated into the state representation and reward function of reinforcement learning (RL)to optimize the recommendation strategy. Comparative experiments with nine recommendation methods on two online education datasets show that GEPR can fully combine the semantic information of resources and group knowledge information, capture users’ personalized needs, and improve recommendation accuracy.