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Enabling Direct Interest-Aware Audience Selection

Ariel Fuxman, Anitha Kannan, Jessie Li, and Panayiotis Tsaparas

Abstract

Advertisers typically know the interests of the users that they wish to target. However, today’s online advertising systems are unable to leverage this. 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, our system also enables seamless addition of interest labels that the advertiser may provide.

The system runs at scale on millions of users and hundreds of millions of queries. We performed a thorough experimental evaluation that shows that our approach leads to a significant increase of over 50% in the probability that a user will click on an ad related to a given interest. The evaluation is fully automated and performed at a large scale, on 150,000 users using 2 months of ad data and user histories consisting of 16 months of query activity.

Details

Publication typeInproceedings
Published inInternational Conference on Information and Knowledge Management
PublisherACM
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