Metacognition-driven user-to-project recommendation for online education services

Various online learning platforms have accumulated a large number of users who are learning or have completed their studies. In which, online education mostly provides services for theoretical learning. A vital issue is how to bridge theoretical learning and practical projects for improving users’ competence. To address this issue, we propose a novel M etacognition-driven U ser-to-P roject recommendation approach for online E ducation S ervices (MUP-ES). We closely model theoretical content and practical project by constructing H eterogeneous I nformation N etworks (HINs) covering both user learning outcomes (learned courses, acquired knowledge concepts, etc.) and project knowledge requirements. We design an attention-based metacognitive information aggregation process to efficiently refine the structural and semantic information of HINs and match users to appropriate projects. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences. MUP-ES provides two major components, path filtering and information aggregation. The path filtering module filters the impurity information in the path through the attention mechanism, and enhances the weight of user learning information that is more suitable for the current project. The information aggregation module learns the neighborhood information of the nodes in the path through the multi-head self-attention mechanism, which aggregates the rich user-related information to the project nodes. Finally, MUP-ES predicts the project’s match score with real users and tests the model’s ability to mitigate the cold-start problem. Extensive experiments on two real-world education datasets show that MUP-ES achieves more accurate prediction results than state-of-the-art baselines.