Abstract Facing the complex and diverse interests of college students, current career planning recommendation methods often suffer from low accuracy. To tackle this, we propose a novel knowledge graph-integrated recommendation model. Our approach begins by constructing a comprehensive career planning knowledge graph. We then project sparse user and item vectors into a dense latent space. Leveraging triplet information from the graph, we form project neighborhoods and employ a Graph Convolutional Network to adaptively aggregate neighbor vectors, deriving enriched representations for students and careers. A key innovation is the fusion of these graph-based representations with those from a generalized matrix factorization component. The concatenated vectors are fed into a fully connected layer to output the final recommendation score. Extensive comparative experiments on three real-world career planning datasets demonstrate the superiority of our model, showing significant improvements in recommendation accuracy of at least 2.04%, 2.26%, and 1.34%, respectively, over state-of-the-art methods.
Xu et al. (Sun,) studied this question.