Research

Current Projects:

• SciPRec (Scientific Paper Recommendation) - Scientific paper recommendation is nowadays an interesting research area that applies ideas from different domains. The increasing amount of rapidly published scientific papers poses a challenge for researchers to discover and keep track of important and relevant research results. Therefore, it is desirable to design recommender systems based on advanced techniques that deliver useful recommendations. In this project we study, investigate and model machine learning approaches for scientific paper recommendation.
• SeRela: Exploiting Semantic Relationships for Cross-Domain Recommendations - Cross-domain recommender systems (CDRS) aim at generating recommendations that span across multiple domains by transferring knowledge from a source to a target domain, with the assumption that there exists strong dependencies between items of those domains. When RDF is used as the input model, the semantic relationships which exist in the paths connecting items or their attributes can be exploited to enhance the quality of recommendations. In this project we investigate techniques for cross-domain recommendations which exploits the semantics encoded in the knowledge-graph.
• RecRDF4J (Recommendations on top of RDF4J) - Recommender Systems aim at predicting the taste of a user towards a set of non-consumed items. Whereas the consumption relationship between users and items can be modelled in various forms, e.g. a matrix, there exist interconnections between items or between their attributes that are relevant to produce good recommendations. By modelling the problem as an RDF-graph, one can leverage the semantics encoded in those interconnections. This project is an extension of RDF4J, a Java framework for processing RDF data, to make it possible to generate recommendations on top of RDF-graphs.
• Distributed Processing of Semantic Data (DiPoS) - Hadoop and its surrounding ecosystem have become the de-facto industry gold standard for Big Data applications used by leading internet companies such as Facebook, Amazon, Twitter, etc. Fundamentally, Hadoop is a general purpose cluster computing platform that is not targeted to any specific application field. We investigate the adoption of these technologies to solve challenges related to the processing of large-scale semantic data to make the vision of a Semantic Web become reality.
• Music Sensing in a Social Context (MuSe) - Nowadays, more data is available for the long tail of users' tastes. In the project MuSe (Music Sensing in a Social Context), we aim to leverage this information to increase the quality of music recommendations. The intuition is that it is more interesting to learn about a new – i.e. so far unknown – artist than being offered the top-k artists of the current music charts.
• RDF Data Description Language (RDD) - Although the intention of RDF is to provide an open, minimally constraining way for representing information, in many scenarios guarantees on the structure and values of an RDF data are essential. The RDD language constitutes a user-friendly tool to specify instance-level constraints that hold in RDD data sets. Making constraints explicit by means of RDDs helps in asserting and maintaining data quality, opens up new optimization opportunities for query engines, and makes query formulation a lot easier for users and system developers.

To former projects