  Explanations are well-known to improve recommender systems' transparency.
These explanations may be local, explaining an individual recommendation, or
global, explaining the recommender model in general. Despite their widespread
use, there has been little investigation into the relative benefits of these
two approaches. Do they provide the same benefits to users, or do they serve
different purposes? We conducted a 30-participant exploratory study and a
30-participant controlled user study with a research-paper recommender system
to analyze how providing participants local, global, or both explanations
influences user understanding of system behavior. Our results provide evidence
suggesting that both explanations are more helpful than either alone for
explaining how to improve recommendations, yet both appeared less helpful than
global alone for efficiency in identifying false positives and negatives.
However, we note that the two explanation approaches may be better compared in
the context of a higher-stakes or more opaque domain.
