Context-aware academic collaborator recommendation
Collaborator Recommendation is a useful application in exploiting big academic data.
However, existing works leave out the contextual restriction (ie, research topics) of people's …
However, existing works leave out the contextual restriction (ie, research topics) of people's …
mg2vec: Learning relationship-preserving heterogeneous graph representations via metagraph embedding
Given that heterogeneous information networks (HIN) encompass nodes and edges
belonging to different semantic types, they can model complex data in real-world scenarios …
belonging to different semantic types, they can model complex data in real-world scenarios …
Elpis: Graph-based similarity search for scalable data science
I Azizi, K Echihabi, T Palpanas - Proceedings of the VLDB Endowment, 2023 - dl.acm.org
The recent popularity of learned embeddings has fueled the growth of massive collections of
high-dimensional (high-d) vectors that model complex data. Finding similar vectors in these …
high-dimensional (high-d) vectors that model complex data. Finding similar vectors in these …
Attentive meta-graph embedding for item recommendation in heterogeneous information networks
Heterogeneous information network (HIN) has become increasingly popular to be exploited
in recommender systems, since it contains abundant semantic information to help generate …
in recommender systems, since it contains abundant semantic information to help generate …
Preference-aware graph attention networks for cross-domain recommendations with collaborative knowledge graph
Knowledge graphs (KGs) can provide users with semantic information and relations among
numerous entities and nodes, which can greatly facilitate the performance of recommender …
numerous entities and nodes, which can greatly facilitate the performance of recommender …
Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive …
Recommender Systems (RS) are established to deal with the preferences of users to
enhance their experience and interest in innumerable online applications by streamlining …
enhance their experience and interest in innumerable online applications by streamlining …
Knowledge-enhanced causal reinforcement learning model for interactive recommendation
Owing to its inherently dynamic nature and economical training cost, offline reinforcement
learning (RL) is typically employed to implement an interactive recommender system (IRS) …
learning (RL) is typically employed to implement an interactive recommender system (IRS) …
Explainable graph-based fraud detection via neural meta-graph search
Though graph neural networks (GNNs)-based fraud detectors have received remarkable
success in identifying fraudulent activities, few of them pay equal attention to models' …
success in identifying fraudulent activities, few of them pay equal attention to models' …
KGTORe: tailored recommendations through knowledge-aware GNN models
Knowledge graphs (KG) have been proven to be a powerful source of side information to
enhance the performance of recommendation algorithms. Their graph-based structure …
enhance the performance of recommendation algorithms. Their graph-based structure …
Meta-path guided graph attention network for explainable herb recommendation
Y Jin, W Ji, Y Shi, X Wang, X Yang - Health Information Science and …, 2023 - Springer
Abstract Traditional Chinese Medicine (TCM) has been widely adopted in clinical practice by
Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in …
Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in …