  Document embeddings and similarity measures underpin content-based
recommender systems, whereby a document is commonly represented as a single
generic embedding. However, similarity computed on single vector
representations provides only one perspective on document similarity that
ignores which aspects make two documents alike. To address this limitation,
aspect-based similarity measures have been developed using document
segmentation or pairwise multi-class document classification. While
segmentation harms the document coherence, the pairwise classification approach
scales poorly to large scale corpora. In this paper, we treat aspect-based
similarity as a classical vector similarity problem in aspect-specific
embedding spaces. We represent a document not as a single generic embedding but
as multiple specialized embeddings. Our approach avoids document segmentation
and scales linearly w.r.t.the corpus size. In an empirical study, we use the
Papers with Code corpus containing 157,606 research papers and consider the
task, method, and dataset of the respective research papers as their aspects.
We compare and analyze three generic document embeddings, six specialized
document embeddings and a pairwise classification baseline in the context of
research paper recommendations. As generic document embeddings, we consider
FastText, SciBERT, and SPECTER. To compute the specialized document embeddings,
we compare three alternative methods inspired by retrofitting, fine-tuning, and
Siamese networks. In our experiments, Siamese SciBERT achieved the highest
scores. Additional analyses indicate an implicit bias of the generic document
embeddings towards the dataset aspect and against the method aspect of each
research paper. Our approach of aspect-based document embeddings mitigates
potential risks arising from implicit biases by making them explicit.
