Personalized Research Paper Recommendation Based On Heterogeneous Network Embedding

The growth of Web during the last three decades contributed significantly into sharing informa-tion among the users.Products,documents,books and other resources are among daily searches on online platforms.This led to the need of creation of models and tools that support users with relevant content to their searches and needs.Finding relevant research papers that can support researchers is one of the topics in the sphere of these recommender systems that is becoming popular.This is due the fact that the huge amount of research papers published every year is increasing rapidly.Research community developed and published multiple models in this domain that aim to support users with papers that are relevant to their research.These systems exploit users' preference dynamics and pro-duce personalized results.The literature exhibits that researchers' preferences dynamics correspond to different information sources in the heterogeneous information networks,which include author information,topics,venue,labels,contents and contextual information.Therefore,it is pertinent to further exploit and analyze the impact of semantic relations among the heterogeneous information networks to achieve more personalized recommendations.To this point,with this dissertation,we employ semantic relations and contextual information corresponds to these heterogeneous networks to further understand and support researchers.An extensive survey of the literature reveals that existing models are unable to effectively exploit the semantics and contextual information corresponding to the heterogeneous information network objects.In particular,they fail to exploit prominent information factors,namely: papers' citation proximity,author information,topical relevance,venue information,researchers' preference dynam-ics,semantic relations,and labels to produce quality recommendations.These models encounter problems such as cold start paper,network sparsity,and lack of personalization.Besides,existing models do not consider the significance and distinct properties of semantic relations between the ob-jects of heterogeneous information networks and are therefore limited to produce interpretable and justifiable results.Considering the limitations of existing models,this dissertation presents the following contribu-tions:1.The first contribution of this dissertation is a comprehensive survey of state-of-the-art paper rec-ommendation models.The study examines explored models based on data factors used,data representation methods adopted,methodologies and models utilized,recommendation types,problems addressed,and personalization.Besides,the study presents the domain's popular issues and recommendations,adopted evaluation metrics,and quantitative analysis of the ex-perimental procedures adopted in the explored models.2.The second contribution is a weighted probabilistic paper recommendation model termed as PR-HNE,which jointly learns researchers' and papers' dynamics by encoding information from six information networks into a joint latent space.The learned embeddings are utilized to pro-duce citation recommendations.The model exploits auxiliary information sources to produce personalized results and overcome the cold-start paper problem.3.The third contribution attempts to overcome the network sparsity problem and termed as Global Citation Recommendation employing Generative Adversarial Network(GCR-GAN).The model exploits the Heterogeneous Bibliographic Network(HBN)to generate personalized citation rec-ommendations.The proposed model utilizes semantic relations corresponding to the objects of the heterogeneous bibliographic network and captures network structure proximity employing the Scientific Paper Embeddings using Citation-informed Transformers(SPECTER)and De-noising Auto-encoder networks to learn semantic-preserving graph representations.The model employs domain-specific and citation-informed document embedding model along with GAN and alleviates the network sparsity and lack of personalization problems.4.The fourth contribution focuses on exploiting the significance of semantic relations and salient factors and proposes a model called GCR-MHNE,which employs a Multi-View Heteroge-neous Network Embedding method to generate personalized recommendations.Specifically,it exploits semantic relations between papers based on citations,venue information,topical rele-vance,authors' information,and relevant labels to learn their vector representations.Moreover,the model captures the most influential features related to each semantic relation employing an attention mechanism.As the model capture the distinct properties of semantic relations in the network and capture salient factors employing an attention mechanism,its recommendations are context-preserving and interpretable.