|
SimHash
In computer science, SimHash is a technique for quickly estimating how Similarity measure, similar two sets are. The algorithm is used by the Google Web crawler, Crawler to find near duplicate pages. It was created by Moses Charikar. In 2021 Google announced its intent to also use the algorithm in their newly created Federated Learning of Cohorts, FLoC (Federated Learning of Cohorts) system. Evaluation and benchmarks A large scale evaluation has been conducted by Google in 2006 to compare the performance of Minhash and Simhash algorithms. In 2007 Google reported using Simhash for duplicate detection for web crawling and using Minhash and Locality-sensitive hashing, LSH for Google News personalization. . See also * MinHash * w-shingling * Count–min sketch * Locality-sensitive hashing References External linksSimhash Princeton Paper [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Federated Learning Of Cohorts
Federated Learning of Cohorts (FLoC) is a type of web tracking. It groups people into "cohorts" based on their browsing history for the purpose of Targeted advertising, interest-based advertising. FLoC was being developed as a part of Google's Privacy Sandbox initiative, which includes several other advertising-related technologies with bird-themed names. Despite "federated learning" in the name, FLoC does not utilize any federated learning. Google began testing the technology in Google Chrome, Chrome 89 released in March 2021 as a replacement for third-party cookies. By April 2021, every major browser aside from Google Chrome that is based on Google's open-source Chromium (web browser), Chromium platform had declined to implement FLoC. The technology was criticized on privacy grounds by groups including the Electronic Frontier Foundation and DuckDuckGo, and has been described as Anti-competitive practices, anti-competitive; it generated an AntiTrust, antitrust response in mult ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
