  In an online contract selection problem there is a seller which offers a set
of contracts to sequentially arriving buyers whose types are drawn from an
unknown distribution. If there exists a profitable contract for the buyer in
the offered set, i.e., a contract with payoff higher than the payoff of not
accepting any contracts, the buyer chooses the contract that maximizes its
payoff. In this paper we consider the online contract selection problem to
maximize the sellers profit. Assuming that a structural property called ordered
preferences holds for the buyer's payoff function, we propose online learning
algorithms that have sub-linear regret with respect to the best set of
contracts given the distribution over the buyer's type. This problem has many
applications including spectrum contracts, wireless service provider data plans
and recommendation systems.
