  Recommender systems assist legal professionals in finding relevant literature
for supporting their case. Despite its importance for the profession, legal
applications do not reflect the latest advances in recommender systems and
representation learning research. Simultaneously, legal recommender systems are
typically evaluated in small-scale user study without any public available
benchmark datasets. Thus, these studies have limited reproducibility. To
address the gap between research and practice, we explore a set of
state-of-the-art document representation methods for the task of retrieving
semantically related US case law. We evaluate text-based (e.g., fastText,
Transformers), citation-based (e.g., DeepWalk, Poincar\'e), and hybrid methods.
We compare in total 27 methods using two silver standards with annotations for
2,964 documents. The silver standards are newly created from Open Case Book and
Wikisource and can be reused under an open license facilitating
reproducibility. Our experiments show that document representations from
averaged fastText word vectors (trained on legal corpora) yield the best
results, closely followed by Poincar\'e citation embeddings. Combining fastText
and Poincar\'e in a hybrid manner further improves the overall result. Besides
the overall performance, we analyze the methods depending on document length,
citation count, and the coverage of their recommendations. We make our source
code, models, and datasets publicly available at
https://github.com/malteos/legal-document-similarity/.
