17 citations · 18 across the 4 of their papers we have counts for
4 papers
The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results
Theis E. Jendal, Matteo Lissandrini, Peter Dolog +1
Inductive Recommender Systems are capable of recommending for new users and with new items thus avoiding the need to retrain after new data reaches the system. However, these metho…
Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions
Theis E. Jendal, Matteo Lissandrini, Peter Dolog +1
Using graph models with relational information in recommender systems has shown promising results. Yet, most methods are transductive, i.e., they are based on dimensionality reduct…
MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings
Anders H. Brams, Anders L. Jakobsen, Theis E. Jendal +3
Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, ex…
A Foundation for Spatio-Textual-Temporal Cube Analytics (Extended Version)
Mohsin Iqbal, Matteo Lissandrini, Torben Bach Pederse
Large amounts of spatial, textual, and temporal data are being produced daily. This is data containing an unstructured component (text), a spatial component (geographic position),…