Scalable Audience Reach Estimation in Real-time Online Advertising

Online advertising has been introduced as one of the most efficient methods of advertising throughout the recent years. Yet, advertisers are concerned about the efficiency of their online advertising campaigns and consequently, would like to restrict their ad impressions to certain websites and/or certain groups of audience. These restrictions, known as targeting criteria, limit the reachability for better performance. This trade-off between reachability and performance illustrates a need for a forecasting system that can quickly predict/estimate (with good accuracy) this trade-off. Designing such a system is challenging due to (a) the huge amount of data to process, and, (b) the need for fast and accurate estimates. In this paper, we propose a distributed fault tolerant system that can generate such estimates fast with good accuracy. The main idea is to keep a small representative sample in memory across multiple machines and formulate the forecasting problem as queries against the sample. The key challenge is to find the best strata across the past data, perform multivariate stratified sampling while ensuring fuzzy fall-back to cover the small minorities. Our results show a significant improvement over the uniform and simple stratified sampling strategies which are currently widely used in the industry

Query-Based Sampling using Only Snippets

Query-based sampling is a popular approach to model the content of an uncooperative server. It works by sending queries to the server and downloading the returned documents in the search results in full. This sample of documents then represents the server’s content. We present an approach that uses the document snippets as samples instead of downloading entire documents. This yields more stable results at the same amount of bandwidth usage as the full document approach. Additionally, we show that using snippets does not necessarily incur more latency, but can actually save time

Query-Based Sampling using Snippets

Query-based sampling is a commonly used approach to model the content of servers. Conventionally, queries are sent to a server and the documents in the search results returned are downloaded in full as representation of the server’s content. We present an approach that uses the document snippets in the search results as samples instead of downloading the entire documents. We show this yields equal or better modeling performance for the same bandwidth consumption depending on collection characteristics, like document length distribution and homogeneity. Query-based sampling using snippets is a useful approach for real-world systems, since it requires no extra operations beyond exchanging queries and search results