Abstract Linked Open Data has been recognized as a valuable source for background
information in many data mining and information retrieval tasks. However, most of the …

Knowledge Graphs have proven to be extremely valuable to recommender systems, as they
enable hybrid graph-based recommendation models encompassing both collaborative and …

The emergence of various disruptive technologies such as big data, Internet of Things, and
artificial intelligence have instigated our society to generate enormous volumes of data. The …

Abstract Knowledge graphs have shown to be highly beneficial to recommender systems,
providing an ideal data structure to generate hybrid recommendations using both content …

Vector space embeddings have been shown to perform well when using RDF data in data
mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local …

In the past years, knowledge graphs have proven to be beneficial for recommender systems,
efficiently addressing paramount issues such as new items and data sparsity. Graph …

A recommender system studies the past behaviour of a user and recommends relevant and
accurate items for the user from a large pool of information. For user 'u'a recommender …

We present multi-modal adversarial autoencoders for recommendation and evaluate them
on two different tasks: citation recommendation and subject label recommendation. We …

Translational models have proven to be accurate and efficient at learning entity and relation
representations from knowledge graphs for machine learning tasks such as knowledge …

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned
with deriving higher-level insights from data. The tasks performed in this field are knowledge …