  To cope with the ever-growing information overload, an increasing number of
digital libraries employ content-based recommender systems. These systems
traditionally recommend related documents with the help of similarity measures.
However, current document similarity measures simply distinguish between
similar and dissimilar documents. This simplification is especially crucial for
extensive documents, which cover various facets of a topic and are often found
in digital libraries. Still, these similarity measures neglect to what facet
the similarity relates. Therefore, the context of the similarity remains
ill-defined. In this doctoral thesis, we explore contextual document similarity
measures, i.e., methods that determine document similarity as a triple of two
documents and the context of their similarity. The context is here a further
specification of the similarity. For example, in the scientific domain,
research papers can be similar with respect to their background, methodology,
or findings. The measurement of similarity in regards to one or more given
contexts will enhance recommender systems. Namely, users will be able to
explore document collections by formulating queries in terms of documents and
their contextual similarities. Thus, our research objective is the development
and evaluation of a recommender system based on contextual similarity. The
underlying techniques will apply established similarity measures and as well as
neural approaches while utilizing semantic features obtained from links between
documents and their text.
