  Automated platforms which support users in finding a mutually beneficial
match, such as online dating and job recruitment sites, are becoming
increasingly popular. These platforms often include recommender systems that
assist users in finding a suitable match. While recommender systems which
provide explanations for their recommendations have shown many benefits,
explanation methods have yet to be adapted and tested in recommending suitable
matches. In this paper, we introduce and extensively evaluate the use of
"reciprocal explanations" -- explanations which provide reasoning as to why
both parties are expected to benefit from the match. Through an extensive
empirical evaluation, in both simulated and real-world dating platforms with
287 human participants, we find that when the acceptance of a recommendation
involves a significant cost (e.g., monetary or emotional), reciprocal
explanations outperform standard explanation methods which consider the
recommendation receiver alone. However, contrary to what one may expect, when
the cost of accepting a recommendation is negligible, reciprocal explanations
are shown to be less effective than the traditional explanation methods.
