Abstract: In the diverse domains of the Internet， facing the ever-increasing volume of data， there is a growing need for recommendation systems to provide users with personalized information. Utilizing knowledge graphs can enhance the accuracy， diversity， and interpretability of these systems. Addressing the current methods’ limitations in accurately capturing genuine user preferences during propagation， and their lack of attention to the utility of semantic information， this paper proposes an advanced paper recommendation algorithm based on semantic features and knowledge graphs. This method employs the BERT model to extract semantic features from paper abstracts， and uses knowledge graphs for collaborative propagation to obtain entity representations of users and items. During propagation， user preferences are accurately transmitted through multi-head attention， and an attention aggregation network is differentiated between entity representation sets at each layer， the importance of initial information is emphasized. Performance evaluations on three public datasets demonstrate that the model proposed in this paper， compared to the selected optimal baseline models， achieves an increase of 0.010、0.018  and 0.007 in AUC， and 0.007 、0.008 and 0.008 in F1 score， respectively， thereby showing the effectiveness and the superiority of the algorithm proposed in this paper. 

Key words:  , recommendation system； knowledge graph； attention network； semantic fusion