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Earlier this month, the Google company announced a new upgrade to its technology, which enables it to develop new algorithms faster and with more ease. Consequently, Google is able to rapidly create new anti-spam algorithms, improve natural language processing and Google SEO ranking algorithms, and deploy them into production faster than ever before.
There have been several spam-fighting algorithms rolled out by Google throughout June and July of this year. These developments followed the publication of this new technology in May 2021.
While the timing could be a coincidence, considering everything that the new version of Keras-based TF-Ranking does, it is important to familiarize oneself with it to understand why Google is releasing new ranking-related updates more frequently.
Google’s TensorFlow Ranking:
The new version of Google’s TF-Ranking algorithm can be used to improve neural learning to rank algorithms and natural language processing algorithms like BERT. It is a powerful way to develop new algorithms and to amplify existing ones in a way that is highly efficient. Google describes Tensor Flow as a machine learning platform.
According to a YouTube video from 2019, the first version of Tensor Flow Ranking was as follows: “The first open source deep learning library for learning to rank (LTR) at scale.”
This platform made it possible to rank documents relevant to the search terms they contained. Previously relevant documents were ranked against each other in a process referred to as pairwise Powerful Ranking Algorithms.
Compared to another item, the likelihood of one document being relevant was determined. Comparing pairs of documents was compared rather than the entire list. Multi-item scoring is an innovation of TF-Ranking, because it enables comparison of the entire list of documents at once. Better ranking decisions can be made with this approach.
Development of powerful new algorithms:
According to Google’s AI Blog article, the new TF-Ranking is a major release that makes it easier than ever to set up learning to rank (LTR) models and put them into production quicker.
Thus, Google can create new algorithms and add them to search faster than ever before. The article states: “Our native Keras ranking model has a brand-new workflow design, including a flexible ModelBuilder, a DatasetBuilder to set up training data, and a Pipeline to train the model with the provided dataset.
These components make building a customized LTR model easier than ever and facilitate rapid exploration of new model structures for production and research.” The BERT algorithm is a machine learning approach to natural language processing.
It’s a way to understand search queries and web page content. In the last few years, BERT was one of Google and Bing’s most important updates. According to the article, combining TF-R with BERT to optimize the ordering of list inputs produced “significant improvements.”
The fact that the results were significant is important because it indicates the likelihood that something similar is currently in use.
According to Google:
“Our experience shows that this TFR-BERT architecture delivers significant improvements in pretrained language model performance, leading to state-of-the-art performance for several Powerful Ranking Algorithms tasks…”