PRM-KGED: paper recommender model using knowledge graph embedding and deep neural network

During the past decades, several academic paper recommendation systems were introduced in literature aiming to assist users
in finding relevant papers close to their needs. In particular, those models have used users’ past preferences combined with
papers’ side information to personalize their recommendations. Unfortunately, the majority of those models fail to utilize
the implicit relationship between the crucial elements along with the semantic relations of the nodes in a Heterogeneous
Information Network (HIN), which can further improve the accuracy of the models. In this paper, we propose a research article
recommendation model that aims to tackle those issues by exploiting both textual and graph representations, simultaneously.
The model employs SPECTER document embedding to learn context-preserving research article representations. In particular,
it learns the features from a HIN, which is a knowledge graph of the node entities and their relationships. Then, an attention
module strengthens the semantic feature extraction process and learns enhanced representations. The combined semantic and
structural features are then provided as input to a Deep Neural Network (DNN) in order to learn high-level representations
of query and candidate papers. We have evaluated our model against state-of-the-art models over two popular datasets. The
results indicate a significant improvement in terms of MAP, recall, and MRR.