Enabling direct interest-aware audience selection

Ariel Fuxman, Anitha Kannan, Zhenhui Li, Panayiotis Tsaparas

Abstract: Advertisers typically have a fairly accurate idea of the interests of their target audience. However, today's online advertising systems are unable to leverage this information. The reasons are two-fold. First, there is no agreed upon vocabulary of interests for advertisers and advertising systems to communicate. More importantly, advertising systems lack a mechanism for mapping users to the interest vocabulary. In this paper, we tackle both problems. We present a system for direct interest-aware audience selection. This system takes the query histories of search engine users as input, extracts their interests, and describes them with interpretable labels. The labels are not drawn from a predefined taxonomy, but rather dynamically generated from the query histories, and are thus easy for the advertisers to interpret and use for targeting users. In addition, the system enables seamless addition of interest labels that may be provided by the advertiser.