"Stanford大数据挖掘与广告"

The Stanford University Big Data Mining advertising19.pptx presentation covers the topic of online algorithms in data mining, specifically in the context of search advertising. The presentation discusses the classic model of algorithms, where the entire input is visible and a function is computed based on it, as well as the concept of offline algorithms. In contrast, online algorithms involve seeing the input one piece at a time and making irreversible decisions along the way, which is similar to data stream models. One example used to illustrate online algorithms is bipartite matching, where the objective is to match elements from two disjoint sets, such as girls and boys. The matching process involves making decisions based on the incoming elements, and the goal is to create an optimal matching based on the available information. This example demonstrates the real-world application of online algorithms in the context of search advertising. Overall, the presentation explores the application of online algorithms in the field of data mining, with a focus on their relevance to search advertising. It provides insights into how these algorithms can be used to make decisions based on incoming data, and highlights their importance in optimizing advertising strategies for online platforms. The presentation offers a comprehensive overview of the topic, covering both theoretical concepts and practical examples to illustrate the implementation of online algorithms in the advertising domain. It provides valuable information for students and professionals interested in the intersection of data mining and online advertising.