  In sponsored search advertising (SSA), advertisers need to select keywords
and determine matching types for selected keywords simultaneously, i.e.,
keyword targeting. An optimal keyword targeting strategy guarantees reaching
the right population effectively. This paper aims to address the keyword
targeting problem, which is a challenging task because of the incomplete
information of historical advertising performance indices and the high
uncertainty in SSA environments. First, we construct a data distribution
estimation model and apply a Markov Chain Monte Carlo method to make inference
about unobserved indices (i.e., impression and click-through rate) over three
keyword matching types (i.e., broad, phrase and exact). Second, we formulate a
stochastic keyword targeting model (BB-KSM) combining operations of keyword
selection and keyword matching to maximize the expected profit under the chance
constraint of the budget, and develop a branch-and-bound algorithm
incorporating a stochastic simulation process for our keyword targeting model.
Finally, based on a realworld dataset collected from field reports and logs of
past SSA campaigns, computational experiments are conducted to evaluate the
performance of our keyword targeting strategy. Experimental results show that,
(a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its
superiority as the budget increases, especially in situations with more
keywords and keyword combinations; (c) the proposed data distribution
estimation approach can effectively address the problem of incomplete
performance indices over the three matching types and in turn significantly
promotes the performance of keyword targeting decisions. This research makes
important contributions to the SSA literature and the results offer critical
insights into keyword management for SSA advertisers.
