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.