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by Dstillery Contributor


Improving Delivery by Bidding for What You Can Win

AdKDD 2017 Best Paper


Every day, billions of online advertising slots are bought and sold through real time bidding (RTB). In RTB, publishers sometimes reject bids to deliver ads (impressions) for some brands, due to, for example, direct deals with other brands. Publishers rarely disclose which brands they blacklist to ad buyers. Buyers bidding for a blacklisted brand waste computing resources in a low latency environment and lose an opportunity to show a good ad for a different brand. Here we describe a dynamic system developed at Dstillery that detects these (publisher, brand) combinations based on ad auction win rates and limits bidding for them to the minimum. This system demonstrates 1) a significant increase in the win rates of our bids, 2) a sizable reduction of system load, and 3) effectiveness in finding qualified non-blacklisted brands to replace blacklisted brands to show ads for. The system allows us to deliver more ad impressions while making fewer bids. In addition, we develop and demonstrate a methodology of choosing the optimal exploration exploitation balance of the problem.

Read the full paper written by the Dstillery Data Science team here.