6 citations · 10 across the 3 of their papers we have counts for
4 papers
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
Lukas Galke, Iacopo Vagliano, Ansgar Scherp
Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising,…
Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report
Iacopo Vagliano, Diego Monti, Ansgar Scherp +1
Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users…
Towards Understanding the Evolution of Vocabulary Terms in Knowledge Graphs
Mohammad Abdel-Qader, Ansgar Scherp
Vocabularies are used for modeling data in Knowledge Graphs (KG) like the Linked Open Data Cloud and Wikidata. During their lifetime, the vocabularies of the KGs are subject to cha…
Profiling vs. Time vs. Content: What does Matter for Top-k Publication Recommendation based on Twitter Profiles? - An Extended Technical Report
Chifumi Nishioka, Ansgar Scherp
So far it is unclear how different factors of a scientific publication recommender system based on users' tweets have an influence on the recommendation performance. We examine thr…