A reciprocal recommendation problem is one where the goal of learning is not
just to predict a user's preference towards a passive item (e.g., a book), but
to recommend the targeted user on one side another user from the other side
such that a mutual interest between the two exists. The problem thus is sharply
different from the more traditional items-to-users recommendation, since a good
match requires meeting the preferences of both users. We initiate a rigorous
theoretical investigation of the reciprocal recommendation task in a specific
framework of sequential learning. We point out general limitations, formulate
reasonable assumptions enabling effective learning and, under these
assumptions, we design and analyze a computationally efficient algorithm that
uncovers mutual likes at a pace comparable to those achieved by a clearvoyant
algorithm knowing all user preferences in advance. Finally, we validate our
algorithm against synthetic and real-world datasets, showing improved empirical
performance over simple baselines