  Advertising (ad for short) keyword suggestion is important for sponsored
search to improve online advertising and increase search revenue. There are two
common challenges in this task. First, the keyword bidding problem: hot ad
keywords are very expensive for most of the advertisers because more
advertisers are bidding on more popular keywords, while unpopular keywords are
difficult to discover. As a result, most ads have few chances to be presented
to the users. Second, the inefficient ad impression issue: a large proportion
of search queries, which are unpopular yet relevant to many ad keywords, have
no ads presented on their search result pages. Existing retrieval-based or
matching-based methods either deteriorate the bidding competition or are unable
to suggest novel keywords to cover more queries, which leads to inefficient ad
impressions. To address the above issues, this work investigates to use
generative neural networks for keyword generation in sponsored search. Given a
purchased keyword (a word sequence) as input, our model can generate a set of
keywords that are not only relevant to the input but also satisfy the domain
constraint which enforces that the domain category of a generated keyword is as
expected. Furthermore, a reinforcement learning algorithm is proposed to
adaptively utilize domain-specific information in keyword generation. Offline
evaluation shows that the proposed model can generate keywords that are
diverse, novel, relevant to the source keyword, and accordant with the domain
constraint. Online evaluation shows that generative models can improve coverage
(COV), click-through rate (CTR), and revenue per mille (RPM) substantially in
sponsored search.
